RuVector

A distributed vector database that learns. Store embeddings, query with Cypher, scale horizontally with Raft consensus, and let the index improve itself through Graph Neural Networks.

npx ruvector

All-in-One Package: The core ruvector package includes everything — vector search, graph queries, GNN layers, distributed clustering, AI routing, and WASM support. No additional packages needed.

What Problem Does RuVector Solve?

Traditional vector databases just store and search. When you ask "find similar items," they return results but never get smarter. They don't scale horizontally. They can't route AI requests intelligently.

RuVector is different:

1. Store vectors like any vector DB (embeddings from OpenAI, Cohere, etc.)
2. Query with Cypher like Neo4j (MATCH (a)-[:SIMILAR]->(b) RETURN b)
3. The index learns — GNN layers make search results improve over time
4. Scale horizontally — Raft consensus, multi-master replication, auto-sharding
5. Route AI requests — Semantic routing and FastGRNN neural inference for LLM optimization
6. Compress automatically — 2-32x memory reduction with adaptive tiered compression
7. 39 attention mechanisms — Flash, linear, graph, hyperbolic for custom models
8. Drop into Postgres — pgvector-compatible extension with SIMD acceleration
9. Run anywhere — Node.js, browser (WASM), HTTP server, or native Rust
10. Continuous learning — SONA enables runtime adaptation with LoRA, EWC++, and ReasoningBank

Think of it as: Pinecone + Neo4j + PyTorch + postgres + etcd in one Rust package.

How the GNN Works

Traditional vector search:

Query → HNSW Index → Top K Results

RuVector with GNN:

Query → HNSW Index → GNN Layer → Enhanced Results
                ↑                      │
                └──── learns from ─────┘

The GNN layer:

1. Takes your query and its nearest neighbors
2. Applies multi-head attention to weigh which neighbors matter
3. Updates representations based on graph structure
4. Returns better-ranked results

Over time, frequently-accessed paths get reinforced, making common queries faster and more accurate.

Quick Start

One-Line Install

# Vector database
npm install ruvector
npx ruvector

# Self-learning hooks for Claude Code
npx @ruvector/cli hooks init
npx @ruvector/cli hooks install

Node.js / Browser

# Install
npm install ruvector

# Or try instantly
npx ruvector

Comparison

Feature	RuVector	Pinecone	Qdrant	Milvus	ChromaDB
Latency (p50)	61µs	~2ms	~1ms	~5ms	~50ms
Memory (1M vec)	200MB*	2GB	1.5GB	1GB	3GB
Graph Queries	✅ Cypher	❌	❌	❌	❌
SPARQL/RDF	✅ W3C 1.1	❌	❌	❌	❌
Hyperedges	✅	❌	❌	❌	❌
Dynamic Min-Cut	✅ n^0.12	❌	❌	❌	❌
Self-Learning (GNN)	✅	❌	❌	❌	❌
Runtime Adaptation (SONA)	✅ LoRA+EWC++	❌	❌	❌	❌
AI Agent Routing	✅ Tiny Dancer	❌	❌	❌	❌
Attention Mechanisms	✅ 39 types	❌	❌	❌	❌
Hyperbolic Embeddings	✅ Poincaré+Lorentz	❌	❌	❌	❌
Local Embeddings	✅ 6 models	❌	❌	❌	❌
PostgreSQL Extension	✅ 77+ functions	❌	❌	❌	❌
SIMD Optimization	✅ AVX-512/NEON	Partial	✅	✅	❌
Metadata Filtering	✅	✅	✅	✅	✅
Sparse Vectors	✅ BM25/TF-IDF	✅	✅	✅	❌
Raft Consensus	✅	❌	✅	❌	❌
Multi-Master Replication	✅	❌	❌	✅	❌
Auto-Sharding	✅	✅	✅	✅	❌
Auto-Compression	✅ 2-32x	❌	❌	✅	❌
Snapshots/Backups	✅	✅	✅	✅	❌
Browser/WASM	✅	❌	❌	❌	❌
Standalone Edge DB	✅ rvLite	❌	❌	❌	❌
Differentiable	✅	❌	❌	❌	❌
Multi-Tenancy	✅ Collections	✅	✅	✅	✅
Open Source	✅ MIT	❌	✅	✅	✅

*With PQ8 compression. Benchmarks on Apple M2 / Intel i7.

Features

Core Capabilities

Feature	What It Does	Why It Matters
Vector Search	HNSW index, <0.5ms latency, SIMD acceleration	Fast enough for real-time apps
Cypher Queries	MATCH, WHERE, CREATE, RETURN	Familiar Neo4j syntax
GNN Layers	Neural network on index topology	Search improves with usage
Hyperedges	Connect 3+ nodes at once	Model complex relationships
Metadata Filtering	Filter vectors by properties	Combine semantic + structured search
Collections	Namespace isolation, multi-tenancy	Organize vectors by project/user

Distributed Systems

Feature	What It Does	Why It Matters
Raft Consensus	Leader election, log replication	Strong consistency for metadata
Auto-Sharding	Consistent hashing, shard migration	Scale to billions of vectors
Multi-Master Replication	Write to any node, conflict resolution	High availability, no SPOF
Snapshots	Point-in-time backups, incremental	Disaster recovery
Cluster Metrics	Prometheus-compatible monitoring	Observability at scale

cargo add ruvector-raft ruvector-cluster ruvector-replication

AI & ML

Feature	What It Does	Why It Matters
Tensor Compression	f32→f16→PQ8→PQ4→Binary	2-32x memory reduction
Differentiable Search	Soft attention k-NN	End-to-end trainable
Semantic Router	Route queries to optimal endpoints	Multi-model AI orchestration
Tiny Dancer	FastGRNN neural inference	Optimize LLM inference costs
Adaptive Routing	Learn optimal routing strategies	Minimize latency, maximize accuracy
SONA	Two-tier LoRA + EWC++ + ReasoningBank	Runtime learning without retraining

Attention Mechanisms (@ruvector/attention)

Feature	What It Does	Why It Matters
39 Mechanisms	Dot-product, multi-head, flash, linear, sparse, cross-attention	Cover all transformer and GNN use cases
Graph Attention	RoPE, edge-featured, local-global, neighborhood	Purpose-built for graph neural networks
Hyperbolic Attention	Poincaré ball operations, curved-space math	Better embeddings for hierarchical data
SIMD Optimized	Native Rust with AVX2/NEON acceleration	2-10x faster than pure JS
Streaming & Caching	Chunk-based processing, KV-cache	Constant memory, 10x faster inference

Documentation: Attention Module Docs

Core Attention Mechanisms

Standard attention layers for sequence modeling and transformers.

Mechanism	Complexity	Memory	Best For
DotProductAttention	O(n²)	O(n²)	Basic attention for small-medium sequences
MultiHeadAttention	O(n²·h)	O(n²·h)	BERT, GPT-style transformers
FlashAttention	O(n²)	O(n)	Long sequences with limited GPU memory
LinearAttention	O(n·d)	O(n·d)	8K+ token sequences, real-time streaming
HyperbolicAttention	O(n²)	O(n²)	Tree-like data: taxonomies, org charts
MoEAttention	O(n·k)	O(n·k)	Large models with sparse expert routing

Graph Attention Mechanisms

Attention layers designed for graph-structured data and GNNs.

Mechanism	Complexity	Best For
GraphRoPeAttention	O(n²)	Position-aware graph transformers
EdgeFeaturedAttention	O(n²·e)	Molecules, knowledge graphs with edge data
DualSpaceAttention	O(n²)	Hybrid flat + hierarchical embeddings
LocalGlobalAttention	O(n·k + n)	100K+ node graphs, scalable GNNs

Specialized Mechanisms

Task-specific attention variants for efficiency and multi-modal learning.

Mechanism	Type	Best For
SparseAttention	Efficiency	Long docs, low-memory inference
CrossAttention	Multi-modal	Image-text, encoder-decoder models
NeighborhoodAttention	Graph	Local message passing in GNNs
HierarchicalAttention	Structure	Multi-level docs (section → paragraph)

Hyperbolic Math Functions

Operations for Poincaré ball embeddings—curved space that naturally represents hierarchies.

Function	Description	Use Case
expMap(v, c)	Map to hyperbolic space	Initialize embeddings
logMap(p, c)	Map to flat space	Compute gradients
mobiusAddition(x, y, c)	Add vectors in curved space	Aggregate features
poincareDistance(x, y, c)	Measure hyperbolic distance	Compute similarity
projectToPoincareBall(p, c)	Ensure valid coordinates	Prevent numerical errors

Async & Batch Operations

Utilities for high-throughput inference and training optimization.

Operation	Description	Performance
asyncBatchCompute()	Process batches in parallel	3-5x faster
streamingAttention()	Process in chunks	Fixed memory usage
HardNegativeMiner	Find hard training examples	Better contrastive learning
AttentionCache	Cache key-value pairs	10x faster inference

# Install attention module
npm install @ruvector/attention

# CLI commands
npx ruvector attention list                    # List all 39 mechanisms
npx ruvector attention info flash              # Details on FlashAttention
npx ruvector attention benchmark               # Performance comparison
npx ruvector attention compute -t dot -d 128   # Run attention computation
npx ruvector attention hyperbolic -a distance -v "[0.1,0.2]" -b "[0.3,0.4]"

Deployment

Feature	What It Does	Why It Matters
HTTP/gRPC Server	REST API, streaming support	Easy integration
WASM/Browser	Full client-side support	Run AI search offline
Node.js Bindings	Native napi-rs bindings	No serialization overhead
FFI Bindings	C-compatible interface	Use from Python, Go, etc.
CLI Tools	Benchmarking, testing, management	DevOps-friendly

Benchmarks

Real benchmark results on standard hardware:

Operation	Dimensions	Time	Throughput
HNSW Search (k=10)	384	61µs	16,400 QPS
HNSW Search (k=100)	384	164µs	6,100 QPS
Cosine Distance	1536	143ns	7M ops/sec
Dot Product	384	33ns	30M ops/sec
Batch Distance (1000)	384	237µs	4.2M/sec

Global Cloud Performance (500M Streams)

Production-validated metrics at hyperscale:

Metric	Value	Details
Concurrent Streams	500M baseline	Burst capacity to 25B (50x)
Global Latency (p50)	<10ms	Multi-region + CDN edge caching
Global Latency (p99)	<50ms	Cross-continental with failover
Availability SLA	99.99%	15 regions, automatic failover
Cost per Stream/Month	$0.0035	60% optimized ($1.74M total at 500M)
Regions	15 global	Americas, EMEA, APAC coverage
Throughput per Region	100K+ QPS	Adaptive batching enabled
Memory Efficiency	2-32x compression	Tiered hot/warm/cold storage
Index Build Time	1M vectors/min	Parallel HNSW construction
Replication Lag	<100ms	Multi-master async replication

Compression Tiers

The architecture adapts to your data. Hot paths get full precision and maximum compute. Cold paths compress automatically and throttle resources. Recent data stays crystal clear; historical data optimizes itself in the background.

Think of it like your computer's memory hierarchy—frequently accessed data lives in fast cache, while older files move to slower, denser storage. RuVector does this automatically for your vectors:

Access Frequency	Format	Compression	What Happens
Hot (>80%)	f32	1x	Full precision, instant retrieval
Warm (40-80%)	f16	2x	Slight compression, imperceptible latency
Cool (10-40%)	PQ8	8x	Smart quantization, ~1ms overhead
Cold (1-10%)	PQ4	16x	Heavy compression, still fast search
Archive (<1%)	Binary	32x	Maximum density, batch retrieval

No configuration needed. RuVector tracks access patterns and automatically promotes/demotes vectors between tiers. Your hot data stays fast; your cold data shrinks.

Use Cases

RAG (Retrieval-Augmented Generation)

const context = ruvector.search(questionEmbedding, 5);
const prompt = `Context: ${context.join('\n')}\n\nQuestion: ${question}`;

Recommendation Systems

MATCH (user:User)-[:VIEWED]->(item:Product)
MATCH (item)-[:SIMILAR_TO]->(rec:Product)
RETURN rec ORDER BY rec.score DESC LIMIT 10

Knowledge Graphs

MATCH (concept:Concept)-[:RELATES_TO*1..3]->(related)
RETURN related

Installation

Platform	Command
npm	npm install ruvector
npm (SONA)	npm install @ruvector/sona
Browser/WASM	npm install ruvector-wasm
Rust	cargo add ruvector-core ruvector-graph ruvector-gnn
Rust (SONA)	cargo add ruvector-sona

Documentation

Topic	Link
Getting Started	docs/guides/GETTING_STARTED.md
Cypher Reference	docs/api/CYPHER_REFERENCE.md
GNN Architecture	docs/gnn/gnn-layer-implementation.md
Node.js API	crates/ruvector-gnn-node/README.md
WASM API	crates/ruvector-gnn-wasm/README.md
Performance Tuning	docs/optimization/PERFORMANCE_TUNING_GUIDE.md
API Reference	docs/api/

Crates

All crates are published to crates.io under the ruvector-* namespace.

Core Crates

Crate	Description	crates.io
ruvector-core	Vector database engine with HNSW indexing
ruvector-collections	Collection and namespace management
ruvector-filter	Vector filtering and metadata queries
ruvector-metrics	Performance metrics and monitoring
ruvector-snapshot	Snapshot and persistence management

Graph & GNN

Crate	Description	crates.io
ruvector-graph	Hypergraph database with Neo4j-style Cypher
ruvector-graph-node	Node.js bindings for graph operations
ruvector-graph-wasm	WASM bindings for browser graph queries
ruvector-gnn	Graph Neural Network layers and training
ruvector-gnn-node	Node.js bindings for GNN inference
ruvector-gnn-wasm	WASM bindings for browser GNN

Attention Mechanisms

Crate	Description	crates.io
ruvector-attention	39 attention mechanisms (Flash, Hyperbolic, MoE, Graph)
ruvector-attention-node	Node.js bindings for attention mechanisms
ruvector-attention-wasm	WASM bindings for browser attention
ruvector-attention-cli	CLI for attention testing and benchmarking

Distributed Systems

Crate	Description	crates.io
ruvector-cluster	Cluster management and coordination
ruvector-raft	Raft consensus implementation
ruvector-replication	Data replication and synchronization

AI Agent Routing (Tiny Dancer)

Crate	Description	crates.io
ruvector-tiny-dancer-core	FastGRNN neural inference for AI routing
ruvector-tiny-dancer-node	Node.js bindings for AI routing
ruvector-tiny-dancer-wasm	WASM bindings for browser AI routing

Router (Semantic Routing)

Crate	Description	crates.io
ruvector-router-core	Core semantic routing engine
ruvector-router-cli	CLI for router testing and benchmarking
ruvector-router-ffi	FFI bindings for other languages
ruvector-router-wasm	WASM bindings for browser routing

Dynamic Min-Cut (December 2025 Breakthrough)

Crate	Description	crates.io
ruvector-mincut	Subpolynomial fully-dynamic min-cut (arXiv:2512.13105)
ruvector-mincut-node	Node.js bindings for min-cut
ruvector-mincut-wasm	WASM bindings for browser min-cut

First deterministic exact fully-dynamic min-cut with verified n^0.12 subpolynomial update scaling:

• Brain connectivity — Detect Alzheimer's markers by tracking neural pathway changes in milliseconds
• Network resilience — Predict outages before they happen, route around failures instantly
• AI agent coordination — Find communication bottlenecks in multi-agent systems
• Neural network pruning — Identify which connections can be removed without losing accuracy
• 448+ tests, 256-core parallel optimization, 8KB per core (compile-time verified)

use ruvector_mincut::{DynamicMinCut, Graph};

let mut graph = Graph::new();
graph.add_edge(0, 1, 10.0);
graph.add_edge(1, 2, 5.0);

let mincut = DynamicMinCut::new(&graph);
let (value, cut_edges) = mincut.compute();
// Updates in subpolynomial time as edges change

Self-Learning Query DAG (ruvector-dag)

Crate	Description	crates.io
ruvector-dag	Neural self-learning DAG for automatic query optimization
ruvector-dag-wasm	WASM bindings for browser DAG optimization (58KB gzipped)

Make your queries faster automatically. RuVector DAG learns from every query execution and continuously optimizes performance—no manual tuning required.

• 7 Attention Mechanisms: Automatically selects the best strategy (Topological, Causal Cone, Critical Path, MinCut Gated, etc.)
• SONA Learning: Self-Optimizing Neural Architecture adapts in <100μs per query
• MinCut Control: Rising "tension" triggers automatic strategy switching and predictive healing
• 50-80% Latency Reduction: Queries improve over time without code changes

use ruvector_dag::{QueryDag, OperatorNode};
use ruvector_dag::attention::{AttentionSelector, SelectionPolicy};

let mut dag = QueryDag::new();
let scan = dag.add_node(OperatorNode::hnsw_scan(0, "vectors_idx", 64));
let filter = dag.add_node(OperatorNode::filter(1, "score > 0.5"));
dag.add_edge(scan, filter).unwrap();

// System learns which attention mechanism works best
let selector = AttentionSelector::new();
let scores = selector.select_and_apply(SelectionPolicy::Adaptive, &dag)?;

See ruvector-dag README for full documentation.

Standalone Vector Database (rvLite)

Crate	Description	crates.io
rvlite	SQLite-style vector database for browsers & edge

Runs anywhere JavaScript runs — browsers, Node.js, Deno, Bun, Cloudflare Workers, Vercel Edge:

• SQL + SPARQL + Cypher unified query interface
• Zero dependencies — thin orchestration over existing WASM crates
• Self-learning via SONA ReasoningBank integration

import { RvLite } from '@rvlite/wasm';

const db = await RvLite.create();
await db.sql(`CREATE TABLE docs (id SERIAL, embedding VECTOR(384))`);
await db.sparql(`SELECT ?s WHERE { ?s rdf:type ex:Document }`);
await db.cypher(`MATCH (d:Doc)-[:SIMILAR]->(r) RETURN r`);

Self-Optimizing Neural Architecture (SONA)

Crate	Description	crates.io	npm
ruvector-sona	Runtime-adaptive learning with LoRA, EWC++, and ReasoningBank

SONA enables AI systems to continuously improve from user feedback without expensive retraining:

• Two-tier LoRA: MicroLoRA (rank 1-2) for instant adaptation, BaseLoRA (rank 4-16) for long-term learning
• EWC++: Elastic Weight Consolidation prevents catastrophic forgetting
• ReasoningBank: K-means++ clustering stores and retrieves successful reasoning patterns
• Lock-free Trajectories: ~50ns overhead per step with crossbeam ArrayQueue
• Sub-millisecond Learning: <0.8ms per trajectory processing

# Rust
cargo add ruvector-sona

# Node.js
npm install @ruvector/sona

use ruvector_sona::{SonaEngine, SonaConfig};

let engine = SonaEngine::new(SonaConfig::default());
let traj_id = engine.start_trajectory(query_embedding);
engine.record_step(traj_id, node_id, 0.85, 150);
engine.end_trajectory(traj_id, 0.90);
engine.learn_from_feedback(LearningSignal::positive(50.0, 0.95));

// Node.js
const { SonaEngine } = require('@ruvector/sona');

const engine = new SonaEngine(256); // 256 hidden dimensions
const trajId = engine.beginTrajectory([0.1, 0.2, ...]);
engine.addTrajectoryStep(trajId, activations, attention, 0.9);
engine.endTrajectory(trajId, 0.95);

PostgreSQL Extension

Crate	Description	crates.io	npm
ruvector-postgres	pgvector-compatible PostgreSQL extension with SIMD optimization

v0.2.0 — Drop-in replacement for pgvector with 77+ SQL functions, full AVX-512/AVX2/NEON SIMD acceleration (~2x faster than AVX2), HNSW and IVFFlat indexes, 39 attention mechanisms, GNN layers, hyperbolic embeddings (Poincaré + Lorentz), sparse vectors/BM25, W3C SPARQL 1.1 with 50+ RDF functions, local embeddings (6 fastembed models), and self-learning capabilities.

# Docker (recommended)
docker run -d -e POSTGRES_PASSWORD=secret -p 5432:5432 ruvector/postgres:latest

# From source
cargo install cargo-pgrx --version "0.12.9" --locked
cargo pgrx install --release

# CLI tool for management
npm install -g @ruvector/postgres-cli
ruvector-pg install
ruvector-pg vector create table --dim 1536 --index hnsw

See ruvector-postgres README for full SQL API reference and advanced features.

Tools & Utilities

Crate	Description	crates.io
ruvector-bench	Benchmarking suite for vector operations
profiling	Performance profiling and analysis tools
micro-hnsw-wasm	Lightweight HNSW implementation for WASM

WASM Packages

Specialized WebAssembly modules for browser and edge deployment. These packages bring advanced AI and distributed computing primitives to JavaScript/TypeScript with near-native performance.

Installation

# Install individual packages
npm install @ruvector/learning-wasm
npm install @ruvector/economy-wasm
npm install @ruvector/exotic-wasm
npm install @ruvector/nervous-system-wasm
npm install @ruvector/attention-unified-wasm

# Or build from source
cd crates/ruvector-learning-wasm
wasm-pack build --target web

ruvector-learning-wasm

MicroLoRA, BTSP, and HDC for self-learning AI systems.

Ultra-fast Low-Rank Adaptation (LoRA) optimized for WASM execution with <100us adaptation latency. Designed for real-time per-operator learning in query optimization and AI agent systems.

Feature	Performance	Description
MicroLoRA	<100us latency	Rank-2 LoRA matrices for instant weight adaptation
Per-Operator Scoping	Zero-allocation hot paths	Separate adapters for different operator types
Trajectory Tracking	Lock-free buffers	Record learning trajectories for replay

Architecture:

Input Embedding (256-dim)
       |
       v
  +---------+
  | A: d x 2 |  Down projection
  +---------+
       |
       v
  +---------+
  | B: 2 x d |  Up projection
  +---------+
       |
       v
Delta W = alpha * (A @ B)
       |
       v
Output = Input + Delta W

JavaScript/TypeScript Example:

import init, { WasmMicroLoRA } from '@ruvector/learning-wasm';

await init();

// Create MicroLoRA engine (256-dim, alpha=0.1, lr=0.01)
const lora = new WasmMicroLoRA(256, 0.1, 0.01);

// Forward pass with adaptation
const input = new Float32Array(256).fill(0.5);
const output = lora.forward_array(input);

// Adapt based on gradient signal
const gradient = new Float32Array(256).fill(0.1);
lora.adapt_array(gradient);

// Adapt with reward signal for RL
lora.adapt_with_reward(0.8);  // 80% improvement

console.log(`Adaptations: ${lora.adapt_count()}`);
console.log(`Delta norm: ${lora.delta_norm()}`);

ruvector-economy-wasm

CRDT-based autonomous credit economy for distributed compute networks.

P2P-safe concurrent transactions using Conflict-free Replicated Data Types (CRDTs). Features a 10x-to-1x early adopter contribution curve and stake/slash mechanisms for participation incentives.

Feature	Description
CRDT Ledger	G-Counter (earned) + PN-Counter (spent) for P2P consistency
Contribution Curve	10x early adopter multiplier decaying to 1x baseline
Stake/Slash	Participation requirements with slashing for bad actors
Reputation Scoring	Multi-factor: accuracy * uptime * stake_weight
Merkle Verification	SHA-256 state root for quick ledger verification

Architecture:

+------------------------+
|     CreditLedger       |  <-- CRDT-based P2P-safe ledger
|  +------------------+  |
|  | G-Counter: Earned|  |  <-- Monotonically increasing
|  | PN-Counter: Spent|  |  <-- Can handle disputes/refunds
|  | Stake: Locked    |  |  <-- Participation requirement
|  | State Root       |  |  <-- Merkle root for verification
|  +------------------+  |
+------------------------+
          |
          v
+------------------------+
|  ContributionCurve     |  <-- Exponential decay: 10x -> 1x
+------------------------+
          |
          v
+------------------------+
|   ReputationScore      |  <-- accuracy * uptime * stake_weight
+------------------------+

JavaScript/TypeScript Example:

import init, {
  CreditLedger,
  ReputationScore,
  contribution_multiplier
} from '@ruvector/economy-wasm';

await init();

// Create a new ledger for a node
const ledger = new CreditLedger("node-123");

// Earn credits (with early adopter multiplier)
ledger.creditWithMultiplier(100, "task:abc");
console.log(`Balance: ${ledger.balance()}`);
console.log(`Multiplier: ${ledger.currentMultiplier()}x`);

// Stake for participation
ledger.stake(50);
console.log(`Staked: ${ledger.stakedAmount()}`);

// Check multiplier for network compute hours
const mult = contribution_multiplier(50000.0);  // 50K hours
console.log(`Network multiplier: ${mult}x`);  // ~8.5x

// Track reputation
const rep = new ReputationScore(0.95, 0.98, 1000);
console.log(`Composite score: ${rep.composite_score()}`);

// P2P merge with another ledger (CRDT operation)
const otherEarned = new Uint8Array([/* serialized earned counter */]);
const otherSpent = new Uint8Array([/* serialized spent counter */]);
const mergedCount = ledger.merge(otherEarned, otherSpent);

ruvector-exotic-wasm

Exotic AI mechanisms for emergent behavior in distributed systems.

Novel coordination primitives inspired by decentralized governance, developmental biology, and quantum physics.

Mechanism	Inspiration	Use Case
Neural Autonomous Organization (NAO)	DAOs + oscillatory sync	Decentralized AI agent governance
Morphogenetic Network	Developmental biology	Emergent network topology
Time Crystal Coordinator	Quantum time crystals	Robust distributed coordination

NAO Features:

• Stake-weighted quadratic voting
• Oscillatory synchronization for coherence
• Quorum-based consensus (configurable threshold)

Morphogenetic Network Features:

• Cellular differentiation through morphogen gradients
• Emergent network topology via growth/pruning
• Synaptic pruning for optimization

Time Crystal Features:

• Period-doubled oscillations for stable coordination
• Floquet engineering for noise resilience
• Phase-locked agent synchronization

JavaScript/TypeScript Example:

import init, {
  WasmNAO,
  WasmMorphogeneticNetwork,
  WasmTimeCrystal,
  ExoticEcosystem
} from '@ruvector/exotic-wasm';

await init();

// Neural Autonomous Organization
const nao = new WasmNAO(0.7);  // 70% quorum
nao.addMember("agent_1", 100);  // 100 stake
nao.addMember("agent_2", 50);

const propId = nao.propose("Upgrade memory backend");
nao.vote(propId, "agent_1", 0.9);  // 90% approval weight
nao.vote(propId, "agent_2", 0.6);

if (nao.execute(propId)) {
  console.log("Proposal executed!");
}

// Morphogenetic Network
const net = new WasmMorphogeneticNetwork(100, 100);  // 100x100 grid
net.seedSignaling(50, 50);  // Seed signaling cell at center

for (let i = 0; i < 1000; i++) {
  net.grow(0.1);  // 10% growth rate
}
net.differentiate();
net.prune(0.1);  // 10% pruning threshold

// Time Crystal Coordinator
const crystal = new WasmTimeCrystal(10, 100);  // 10 oscillators, 100ms period
crystal.crystallize();

for (let i = 0; i < 200; i++) {
  const pattern = crystal.tick();
  // Use pattern for coordination decisions
}

console.log(`Synchronization: ${crystal.orderParameter()}`);

// Combined Ecosystem (all three working together)
const eco = new ExoticEcosystem(5, 50, 8);  // 5 agents, 50x50 grid, 8 oscillators
eco.crystallize();

for (let i = 0; i < 100; i++) {
  eco.step();
}

console.log(eco.summaryJson());

ruvector-nervous-system-wasm

Bio-inspired neural system components for browser execution.

Component	Performance	Description
BTSP	Immediate	Behavioral Timescale Synaptic Plasticity for one-shot learning
HDC	<50ns bind, <100ns similarity	Hyperdimensional Computing with 10,000-bit vectors
WTA	<1us	Winner-Take-All for instant decisions
K-WTA	<10us	K-Winner-Take-All for sparse distributed coding
Global Workspace	<10us	4-7 item attention bottleneck (Miller's Law)

Hyperdimensional Computing:

• 10,000-bit binary hypervectors
• 10^40 representational capacity
• XOR binding (associative, commutative, self-inverse)
• Hamming distance similarity with SIMD optimization

Biological References:

• BTSP: Bittner et al. 2017 - Hippocampal place fields
• HDC: Kanerva 1988, Plate 2003 - Hyperdimensional computing
• WTA: Cortical microcircuits - Lateral inhibition
• Global Workspace: Baars 1988, Dehaene 2014 - Consciousness

JavaScript/TypeScript Example:

import init, {
  BTSPLayer,
  Hypervector,
  HdcMemory,
  WTALayer,
  KWTALayer,
  GlobalWorkspace,
  WorkspaceItem,
} from '@ruvector/nervous-system-wasm';

await init();

// One-shot learning with BTSP
const btsp = new BTSPLayer(100, 2000.0);  // 100 dim, 2000ms tau
const pattern = new Float32Array(100).fill(0.1);
btsp.one_shot_associate(pattern, 1.0);  // Immediate association
const output = btsp.forward(pattern);

// Hyperdimensional Computing
const apple = Hypervector.random();
const orange = Hypervector.random();
const fruit = apple.bind(orange);  // XOR binding

const similarity = apple.similarity(orange);  // ~0.0 (orthogonal)
console.log(`Similarity: ${similarity}`);  // Random vectors are orthogonal

// HDC Memory
const memory = new HdcMemory();
memory.store("apple", apple);
memory.store("orange", orange);

const results = memory.retrieve(apple, 0.9);  // threshold 0.9
const topK = memory.top_k(fruit, 3);  // top-3 similar

// Instant decisions with WTA
const wta = new WTALayer(1000, 0.5, 0.8);  // 1000 neurons, threshold, inhibition
const activations = new Float32Array(1000);
// ... fill activations ...
const winner = wta.compete(activations);

// Sparse coding with K-WTA
const kwta = new KWTALayer(1000, 50);  // 1000 neurons, k=50 winners
const winners = kwta.select(activations);

// Attention bottleneck with Global Workspace
const workspace = new GlobalWorkspace(7);  // Miller's Law: 7 +/- 2
const item = new WorkspaceItem(
  new Float32Array([1, 2, 3]),  // content
  0.9,  // salience
  1,    // source
  Date.now()  // timestamp
);
workspace.broadcast(item);

ruvector-attention-unified-wasm

Unified API for 18+ attention mechanisms across Neural, DAG, Graph, and SSM domains.

A single WASM interface that routes to the appropriate attention implementation based on your data structure and requirements.

Category	Mechanisms	Best For
Neural	Scaled Dot-Product, Multi-Head, Hyperbolic, Linear, Flash, Local-Global, MoE	Transformers, sequences
DAG	Topological, Causal Cone, Critical Path, MinCut-Gated, Hierarchical Lorentz, Parallel Branch, Temporal BTSP	Query DAGs, workflows
Graph	GAT, GCN, GraphSAGE	GNNs, knowledge graphs
SSM	Mamba	Long sequences, streaming

Mechanism Selection:

+------------------+     +-------------------+
|   Your Data      | --> | UnifiedAttention  | --> Optimal Mechanism
+------------------+     +-------------------+
                               |
        +----------------------+----------------------+
        |                      |                      |
   +----v----+           +-----v-----+          +-----v----+
   | Neural  |           |    DAG    |          |  Graph   |
   +---------+           +-----------+          +----------+
   | dot_prod|           | topological|         | gat      |
   | multi_hd|           | causal_cone|         | gcn      |
   | flash   |           | mincut_gtd |         | graphsage|
   +---------+           +-----------+          +----------+

JavaScript/TypeScript Example:

import init, {
  UnifiedAttention,
  availableMechanisms,
  getStats,
  softmax,
  temperatureSoftmax,
  cosineSimilarity,
  // Neural attention
  ScaledDotProductAttention,
  MultiHeadAttention,
  // DAG attention
  TopologicalAttention,
  MinCutGatedAttention,
  // Graph attention
  GraphAttention,
  // SSM
  MambaSSM,
} from '@ruvector/attention-unified-wasm';

await init();

// List all available mechanisms
console.log(availableMechanisms());
// { neural: [...], dag: [...], graph: [...], ssm: [...] }

console.log(getStats());
// { total_mechanisms: 18, neural_count: 7, dag_count: 7, ... }

// Unified selector - routes to appropriate implementation
const attention = new UnifiedAttention("multi_head");
console.log(`Category: ${attention.category}`);  // "neural"
console.log(`Supports sequences: ${attention.supportsSequences()}`);  // true
console.log(`Supports graphs: ${attention.supportsGraphs()}`);  // false

// For DAG structures
const dagAttention = new UnifiedAttention("topological");
console.log(`Category: ${dagAttention.category}`);  // "dag"
console.log(`Supports graphs: ${dagAttention.supportsGraphs()}`);  // true

// Hyperbolic attention for hierarchical data
const hypAttention = new UnifiedAttention("hierarchical_lorentz");
console.log(`Supports hyperbolic: ${hypAttention.supportsHyperbolic()}`);  // true

// Utility functions
const logits = [1.0, 2.0, 3.0, 4.0];
const probs = softmax(logits);
console.log(`Probabilities sum to: ${probs.reduce((a, b) => a + b)}`);  // 1.0

// Temperature-scaled softmax (lower = more peaked)
const sharperProbs = temperatureSoftmax(logits, 0.5);

// Cosine similarity
const vecA = [1.0, 0.0, 0.0];
const vecB = [1.0, 0.0, 0.0];
console.log(`Similarity: ${cosineSimilarity(vecA, vecB)}`);  // 1.0

WASM Package Summary

Package	Size Target	Key Features
@ruvector/learning-wasm	<50KB	MicroLoRA (<100us), trajectory tracking
@ruvector/economy-wasm	<100KB	CRDT ledger, 10x->1x curve, stake/slash
@ruvector/exotic-wasm	<150KB	NAO, Morphogenetic, Time Crystal
@ruvector/nervous-system-wasm	<100KB	BTSP, HDC (10K-bit), WTA, Global Workspace
@ruvector/attention-unified-wasm	<200KB	18+ attention mechanisms, unified API

Common Patterns:

// All packages follow the same initialization pattern
import init, { /* exports */ } from '@ruvector/<package>-wasm';
await init();

// Version check
import { version } from '@ruvector/<package>-wasm';
console.log(`Version: ${version()}`);

// Feature discovery
import { available_mechanisms } from '@ruvector/<package>-wasm';
console.log(available_mechanisms());

Self-Learning Intelligence Hooks

Make your AI assistant smarter over time.

When you use Claude Code (or any AI coding assistant), it starts fresh every session. It doesn't remember which approaches worked, which files you typically edit together, or what errors you've seen before.

RuVector Hooks fixes this. It's a lightweight intelligence layer that:

1. Remembers what works — Tracks which agent types succeed for different tasks
2. Learns from mistakes — Records error patterns and suggests fixes you've used before
3. Predicts your workflow — Knows that after editing api.rs, you usually edit api_test.rs
4. Coordinates teams — Manages multi-agent swarms for complex tasks

Think of it as giving your AI assistant a memory and intuition about your codebase.

How It Works

┌─────────────────┐     ┌──────────────────┐     ┌─────────────────┐
│  Claude Code    │────▶│  RuVector Hooks  │────▶│   Intelligence  │
│  (PreToolUse)   │     │   (pre-edit)     │     │      Layer      │
└─────────────────┘     └──────────────────┘     └─────────────────┘
                                                         │
         ┌───────────────────────────────────────────────┘
         ▼
┌─────────────────┐     ┌──────────────────┐     ┌─────────────────┐
│   Q-Learning    │     │  Vector Memory   │     │  Swarm Graph    │
│   α=0.1 γ=0.95  │     │  64-dim embed    │     │  Coordination   │
└─────────────────┘     └──────────────────┘     └─────────────────┘

The hooks integrate with Claude Code's event system:

• PreToolUse → Provides guidance before edits (agent routing, related files)
• PostToolUse → Records outcomes for learning (success/failure, patterns)
• SessionStart/Stop → Manages session state and metrics export

Technical Specifications

Component	Implementation	Details
Q-Learning	Temporal Difference	α=0.1, γ=0.95, ε=0.1 (ε-greedy exploration)
Embeddings	Hash-based vectors	64 dimensions, normalized, cosine similarity
LRU Cache	lru crate	1000 entries, ~10x faster Q-value lookups
Compression	flate2 gzip	70-83% storage reduction, fast compression
Storage	JSON / PostgreSQL	Auto-fallback, 5000 memory entry limit
Cross-platform	Rust + TypeScript	Windows (USERPROFILE), Unix (HOME)

Performance

Metric	Value
Q-value lookup (cached)	<1µs
Q-value lookup (uncached)	~50µs
Memory search (1000 entries)	<5ms
Storage compression ratio	70-83%
Session start overhead	<10ms

Crate/Package	Description	Status
ruvector-cli hooks	Rust CLI with 34 hooks commands
@ruvector/cli hooks	npm CLI with 29 hooks commands

Quick Start

# Rust CLI
cargo install ruvector-cli
ruvector hooks init
ruvector hooks install

# npm CLI
npx @ruvector/cli hooks init
npx @ruvector/cli hooks install

Core Capabilities

Feature	Description	Technical Details
Q-Learning Routing	Routes tasks to best agent with learned confidence scores	TD learning with α=0.1, γ=0.95, ε-greedy exploration
Semantic Memory	Vector-based memory with embeddings for context retrieval	64-dim hash embeddings, cosine similarity, top-k search
Error Learning	Records error patterns and suggests fixes	Pattern matching for E0308, E0433, TS2322, etc.
File Sequences	Predicts next files to edit based on historical patterns	Markov chain transitions, frequency-weighted suggestions
Swarm Coordination	Registers agents, tracks coordination edges, optimizes	Graph-based topology, weighted edges, task assignment
LRU Cache	1000-entry cache for faster Q-value lookups	~10x speedup, automatic eviction, RefCell for interior mutability
Gzip Compression	Storage savings with automatic compression	flate2 fast mode, 70-83% reduction, transparent load/save
Batch Saves	Dirty flag tracking to reduce disk I/O	Only writes when data changes, force_save() override
Shell Completions	Tab completion for all commands	bash, zsh, fish, PowerShell support

Supported Error Codes

The intelligence layer has built-in knowledge for common error patterns:

Language	Error Codes	Auto-Suggested Fixes
Rust	E0308, E0433, E0425, E0277, E0382	Type mismatches, missing imports, borrow checker
TypeScript	TS2322, TS2339, TS2345, TS7006	Type assignments, property access, argument types
Python	ImportError, AttributeError, TypeError	Module imports, attribute access, type errors
Go	undefined, cannot use, not enough arguments	Variable scope, type conversion, function calls

Commands Reference

# Setup
ruvector hooks init [--force] [--postgres]  # Initialize hooks (--postgres for DB schema)
ruvector hooks install                   # Install into Claude settings

# Core
ruvector hooks stats                     # Show intelligence statistics
ruvector hooks session-start [--resume]  # Start/resume a session
ruvector hooks session-end               # End session with metrics

# Memory
ruvector hooks remember -t edit "content"  # Store in semantic memory
ruvector hooks recall "query" -k 5         # Search memory semantically

# Learning
ruvector hooks learn <state> <action> --reward 0.8  # Record trajectory
ruvector hooks suggest <state> --actions "a,b,c"    # Get action suggestion
ruvector hooks route "implement caching" --file src/cache.rs  # Route to agent

# Claude Code Hooks
ruvector hooks pre-edit <file>           # Pre-edit intelligence hook
ruvector hooks post-edit <file> --success  # Post-edit learning hook
ruvector hooks pre-command <cmd>         # Pre-command hook
ruvector hooks post-command <cmd> --success  # Post-command hook
ruvector hooks suggest-context           # UserPromptSubmit context injection
ruvector hooks track-notification        # Track notification patterns
ruvector hooks pre-compact [--auto]      # Pre-compact hook (auto/manual)

# Claude Code v2.0.55+ Features
ruvector hooks lsp-diagnostic --file <f> --severity error  # LSP diagnostics
ruvector hooks suggest-ultrathink "complex task"  # Recommend extended reasoning
ruvector hooks async-agent --action spawn --agent-id <id>  # Async sub-agents

# Intelligence
ruvector hooks record-error <cmd> <stderr>  # Record error pattern
ruvector hooks suggest-fix E0308           # Get fix for error code
ruvector hooks suggest-next <file> -n 3    # Predict next files
ruvector hooks should-test <file>          # Check if tests needed

# Swarm
ruvector hooks swarm-register <id> <type>  # Register agent
ruvector hooks swarm-coordinate <src> <tgt>  # Record coordination
ruvector hooks swarm-optimize "task1,task2"  # Optimize distribution
ruvector hooks swarm-recommend "rust"      # Recommend agent for task
ruvector hooks swarm-heal <agent-id>       # Handle agent failure
ruvector hooks swarm-stats                 # Show swarm statistics

# Optimization (Rust only)
ruvector hooks compress                   # Compress storage (70-83% savings)
ruvector hooks cache-stats                # Show LRU cache statistics
ruvector hooks completions bash           # Generate shell completions

Tutorial: Claude Code Integration

1. Initialize and install hooks:

ruvector hooks init
ruvector hooks install --settings-dir .claude

This creates .claude/settings.json with hook configurations:

{
  "hooks": {
    "PreToolUse": [
      { "matcher": "Edit|Write|MultiEdit", "hooks": ["ruvector hooks pre-edit \"$TOOL_INPUT_FILE_PATH\""] },
      { "matcher": "Bash", "hooks": ["ruvector hooks pre-command \"$TOOL_INPUT_COMMAND\""] }
    ],
    "PostToolUse": [
      { "matcher": "Edit|Write|MultiEdit", "hooks": ["ruvector hooks post-edit ... --success"] },
      { "matcher": "Bash", "hooks": ["ruvector hooks post-command ... --success"] }
    ],
    "SessionStart": ["ruvector hooks session-start"],
    "Stop": ["ruvector hooks session-end --export-metrics"],
    "PreCompact": ["ruvector hooks pre-compact"]
  }
}

All 7 Claude Code hooks covered:

Hook	When It Fires	What RuVector Does
PreToolUse	Before file edit, command, or Task	Suggests agent, shows related files, validates agent assignments
PostToolUse	After file edit or command	Records outcome, updates Q-values, injects context
SessionStart	When session begins/resumes	Loads intelligence, shows stats (startup vs resume)
Stop	When session ends	Saves state, exports metrics
PreCompact	Before context compaction	Preserves critical memories (auto vs manual)
UserPromptSubmit	Before processing user prompt	Injects learned patterns as context
Notification	On system notifications	Tracks notification patterns

Advanced Features:

• Stdin JSON Parsing: Hooks receive full JSON via stdin (session_id, tool_input, tool_response)
• Context Injection: PostToolUse returns additionalContext to inject into Claude's context
• Timeout Optimization: All hooks have optimized timeouts (1-5 seconds vs 60s default)

2. Use routing for intelligent agent selection:

# Route a task to the best agent
$ ruvector hooks route "implement vector search" --file src/lib.rs
{
  "recommended": "rust-developer",
  "confidence": 0.85,
  "reasoning": "learned from 47 similar edits"
}

3. Learn from outcomes:

# Record successful outcome
ruvector hooks learn "edit-rs-lib" "rust-developer" --reward 1.0

# Record failed outcome
ruvector hooks learn "edit-rs-lib" "typescript-dev" --reward -0.5

4. Get error fix suggestions:

$ ruvector hooks suggest-fix E0308
{
  "code": "E0308",
  "type": "type_mismatch",
  "fixes": [
    "Check return type matches function signature",
    "Use .into() or .as_ref() for type conversion",
    "Verify generic type parameters"
  ]
}

Tutorial: Swarm Coordination

1. Register agents:

ruvector hooks swarm-register agent-1 rust-developer --capabilities "rust,async,testing"
ruvector hooks swarm-register agent-2 typescript-dev --capabilities "ts,react,node"
ruvector hooks swarm-register agent-3 reviewer --capabilities "review,security,performance"

2. Record coordination patterns:

# Agent-1 hands off to Agent-3 for review
ruvector hooks swarm-coordinate agent-1 agent-3 --weight 0.9

3. Optimize task distribution:

$ ruvector hooks swarm-optimize "implement-api,write-tests,code-review"
{
  "assignments": {
    "implement-api": "agent-1",
    "write-tests": "agent-1",
    "code-review": "agent-3"
  }
}

4. Handle failures with self-healing:

# Mark agent as failed and redistribute
ruvector hooks swarm-heal agent-2

PostgreSQL Storage (Optional)

For production deployments, use PostgreSQL instead of JSON files:

# Set connection URL
export RUVECTOR_POSTGRES_URL="postgres://user:pass@localhost/ruvector"

# Initialize PostgreSQL schema (automatic)
ruvector hooks init --postgres

# Or apply schema manually
psql $RUVECTOR_POSTGRES_URL -f crates/ruvector-cli/sql/hooks_schema.sql

# Build CLI with postgres feature
cargo build -p ruvector-cli --features postgres

The PostgreSQL backend provides:

• Vector embeddings with native ruvector type
• Q-learning functions (ruvector_hooks_update_q, ruvector_hooks_best_action)
• Swarm coordination tables with foreign key relationships
• Automatic memory cleanup (keeps last 5000 entries)

Scientific OCR (SciPix)

Crate	Description	crates.io
ruvector-scipix	OCR engine for scientific documents, math equations → LaTeX/MathML

SciPix extracts text and mathematical equations from images, converting them to LaTeX, MathML, or plain text. Features GPU-accelerated ONNX inference, SIMD-optimized preprocessing, REST API server, CLI tool, and MCP integration for AI assistants.

# Install
cargo add ruvector-scipix

# CLI usage
scipix-cli ocr --input equation.png --format latex
scipix-cli serve --port 3000

# MCP server for Claude/AI assistants
scipix-cli mcp
claude mcp add scipix -- scipix-cli mcp

ONNX Embeddings

Example	Description	Path
ruvector-onnx-embeddings	Production-ready ONNX embedding generation in pure Rust	examples/onnx-embeddings

ONNX Embeddings provides native embedding generation using ONNX Runtime — no Python required. Supports 8+ pretrained models (all-MiniLM, BGE, E5, GTE), multiple pooling strategies, GPU acceleration (CUDA, TensorRT, CoreML, WebGPU), and direct RuVector index integration for RAG pipelines.

use ruvector_onnx_embeddings::{Embedder, PretrainedModel};

#[tokio::main]
async fn main() -> anyhow::Result<()> {
    // Create embedder with default model (all-MiniLM-L6-v2)
    let mut embedder = Embedder::default_model().await?;

    // Generate embedding (384 dimensions)
    let embedding = embedder.embed_one("Hello, world!")?;

    // Compute semantic similarity
    let sim = embedder.similarity(
        "I love programming in Rust",
        "Rust is my favorite language"
    )?;
    println!("Similarity: {:.4}", sim); // ~0.85

    Ok(())
}

Supported Models:

Model	Dimension	Speed	Best For
AllMiniLmL6V2	384	Fast	General purpose (default)
BgeSmallEnV15	384	Fast	Search & retrieval
AllMpnetBaseV2	768	Accurate	Production RAG

Bindings & Tools

Crate	Description	crates.io
ruvector-node	Main Node.js bindings (napi-rs)
ruvector-wasm	Main WASM bindings for browsers
ruvllm-wasm	LLM integration WASM bindings
ruvector-cli	Command-line interface
ruvector-server	HTTP/gRPC server

Examples

Production-ready examples demonstrating RuVector integration patterns, from cognitive AI substrates to WASM browser deployments.

Example	Description	Type
mincut	6 self-organizing network demos: strange loops, time crystals, causal discovery	Rust
exo-ai-2025	Cognitive substrate with 9 neural-symbolic crates + 11 research experiments	Rust
ruvLLM	LLM integration patterns for RAG and AI agents	Rust
apify	13 Apify actors: trading, memory engine, synth data, market research	npm
google-cloud	GCP deployment templates for Cloud Run, GKE, and Vertex AI	Rust
ultra-low-latency-sim	13+ quadrillion meta-simulations/sec with SIMD	Rust
meta-cognition-spiking-neural-network	Spiking neural network with meta-cognitive learning	npm
onnx-embeddings	Production ONNX embedding generation without Python	Rust
refrag-pipeline	RAG pipeline with vector search and document processing	Rust
scipix	Scientific OCR: equations → LaTeX/MathML with ONNX inference	Rust
spiking-network	Biologically-inspired spiking neural networks	Rust
wasm-react	React integration with WASM vector operations	WASM
wasm-vanilla	Vanilla JS WASM example for browser vector search	WASM
agentic-jujutsu	Quantum-resistant version control for AI agents	Rust
graph	Graph database examples with Cypher queries	Rust
nodejs	Node.js integration examples	Node.js
rust	Core Rust usage examples	Rust

npm Packages

✅ Published

Package	Description	npm
ruvector	All-in-one CLI & package (vectors, graphs, GNN)
@ruvector/core	Core vector database with native Rust bindings
@ruvector/gnn	Graph Neural Network layers & tensor compression
@ruvector/graph-node	Hypergraph database with Cypher queries
@ruvector/tiny-dancer	FastGRNN neural inference for AI agent routing
@ruvector/router	Semantic router with HNSW vector search
@ruvector/agentic-synth	Synthetic data generator for AI/ML
@ruvector/attention	39 attention mechanisms for transformers & GNNs
@ruvector/postgres-cli	CLI for ruvector-postgres extension management
@ruvector/wasm	WASM fallback for core vector DB
@ruvector/gnn-wasm	WASM fallback for GNN layers
@ruvector/graph-wasm	WASM fallback for graph DB
@ruvector/attention-wasm	WASM fallback for attention mechanisms
@ruvector/tiny-dancer-wasm	WASM fallback for AI routing
@ruvector/router-wasm	WASM fallback for semantic router
@ruvector/sona	Self-Optimizing Neural Architecture (SONA)
@ruvector/cluster	Distributed clustering & sharding
@ruvector/server	HTTP/gRPC server mode

Platform-specific native bindings (auto-detected):

• @ruvector/node-linux-x64-gnu, @ruvector/node-linux-arm64-gnu, @ruvector/node-darwin-x64, @ruvector/node-darwin-arm64, @ruvector/node-win32-x64-msvc
• @ruvector/gnn-linux-x64-gnu, @ruvector/gnn-linux-arm64-gnu, @ruvector/gnn-darwin-x64, @ruvector/gnn-darwin-arm64, @ruvector/gnn-win32-x64-msvc
• @ruvector/tiny-dancer-linux-x64-gnu, @ruvector/tiny-dancer-linux-arm64-gnu, @ruvector/tiny-dancer-darwin-x64, @ruvector/tiny-dancer-darwin-arm64, @ruvector/tiny-dancer-win32-x64-msvc
• @ruvector/router-linux-x64-gnu, @ruvector/router-linux-arm64-gnu, @ruvector/router-darwin-x64, @ruvector/router-darwin-arm64, @ruvector/router-win32-x64-msvc
• @ruvector/attention-linux-x64-gnu, @ruvector/attention-linux-arm64-gnu, @ruvector/attention-darwin-x64, @ruvector/attention-darwin-arm64, @ruvector/attention-win32-x64-msvc

🚧 Planned

Package	Description	Status
@ruvector/raft	Raft consensus for distributed ops	Crate ready
@ruvector/replication	Multi-master replication	Crate ready
@ruvector/scipix	Scientific OCR (LaTeX/MathML)	Crate ready

See GitHub Issue #20 for multi-platform npm package roadmap.

# Install all-in-one package
npm install ruvector

# Or install individual packages
npm install @ruvector/core @ruvector/gnn @ruvector/graph-node

# List all available packages
npx ruvector install

const ruvector = require('ruvector');

// Vector search
const db = new ruvector.VectorDB(128);
db.insert('doc1', embedding1);
const results = db.search(queryEmbedding, 10);

// Graph queries (Cypher)
db.execute("CREATE (a:Person {name: 'Alice'})-[:KNOWS]->(b:Person {name: 'Bob'})");
db.execute("MATCH (p:Person)-[:KNOWS]->(friend) RETURN friend.name");

// GNN-enhanced search
const layer = new ruvector.GNNLayer(128, 256, 4);
const enhanced = layer.forward(query, neighbors, weights);

// Compression (2-32x memory savings)
const compressed = ruvector.compress(embedding, 0.3);

// Tiny Dancer: AI agent routing
const router = new ruvector.Router();
const decision = router.route(candidates, { optimize: 'cost' });

Rust

cargo add ruvector-graph ruvector-gnn

use ruvector_graph::{GraphDB, NodeBuilder};
use ruvector_gnn::{RuvectorLayer, differentiable_search};

let db = GraphDB::new();

let doc = NodeBuilder::new("doc1")
    .label("Document")
    .property("embedding", vec![0.1, 0.2, 0.3])
    .build();
db.create_node(doc)?;

// GNN layer
let layer = RuvectorLayer::new(128, 256, 4, 0.1);
let enhanced = layer.forward(&query, &neighbors, &weights);

use ruvector_raft::{RaftNode, RaftNodeConfig};
use ruvector_cluster::{ClusterManager, ConsistentHashRing};
use ruvector_replication::{SyncManager, SyncMode};

// Configure a 5-node Raft cluster
let config = RaftNodeConfig {
    node_id: "node-1".into(),
    cluster_members: vec!["node-1", "node-2", "node-3", "node-4", "node-5"]
        .into_iter().map(Into::into).collect(),
    election_timeout_min: 150,  // ms
    election_timeout_max: 300,  // ms
    heartbeat_interval: 50,     // ms
};
let raft = RaftNode::new(config);

// Auto-sharding with consistent hashing (150 virtual nodes per real node)
let ring = ConsistentHashRing::new(64, 3); // 64 shards, replication factor 3
let shard = ring.get_shard("my-vector-key");

// Multi-master replication with conflict resolution
let sync = SyncManager::new(SyncMode::SemiSync { min_replicas: 2 });

Project Structure

crates/
├── ruvector-core/           # Vector DB engine (HNSW, storage)
├── ruvector-graph/          # Graph DB + Cypher parser + Hyperedges
├── ruvector-gnn/            # GNN layers, compression, training
├── ruvector-tiny-dancer-core/  # AI agent routing (FastGRNN)
├── ruvector-*-wasm/         # WebAssembly bindings
└── ruvector-*-node/         # Node.js bindings (napi-rs)

Contributing

We welcome contributions! See CONTRIBUTING.md.

# Run tests
cargo test --workspace

# Run benchmarks
cargo bench --workspace

# Build WASM
cargo build -p ruvector-gnn-wasm --target wasm32-unknown-unknown

License

MIT License — free for commercial and personal use.

---

Built by rUv • GitHub • npm • Docs

Vector search that gets smarter over time.