RuVector

The vector database that gets smarter the more you use it.

npx ruvector

Most vector databases are static—they store embeddings and search them. That's it. RuVector is different: it learns from every query, runs LLMs locally, scales horizontally, and costs nothing to operate.

	Pinecone/Weaviate	RuVector
Search improves over time	❌	✅ GNN layers learn from usage
Run LLMs locally	❌	✅ ruvllm + RuvLTRA models ($0)
Graph queries (Cypher)	❌	✅ MATCH (a)-[:SIMILAR]->(b)
Self-learning AI hooks	❌	✅ Q-learning, HNSW memory
Real-time graph updates	❌ Rebuild index	✅ Dynamic min-cut (no rebuild)
Horizontal scaling	💰 Paid	✅ Raft consensus, free
Works offline	❌	✅ Browser, edge, embedded

One package. Everything included: vector search, graph queries, GNN learning, distributed clustering, local LLMs, 39 attention mechanisms, and WASM support.

📋 See Full Capabilities (30+ features)

Core Vector Database

#	Capability	What It Does
1	Store vectors	Embeddings from OpenAI, Cohere, local ONNX with HNSW indexing
2	Query with Cypher	Graph queries like Neo4j (MATCH (a)-[:SIMILAR]->(b))
3	The index learns	GNN layers make search results improve over time
4	Hyperbolic HNSW	Hierarchical data in hyperbolic space for better tree structures
5	Compress automatically	2-32x memory reduction with adaptive tiered compression

Distributed Systems

#	Capability	What It Does
6	Raft consensus	Leader election, log replication, fault-tolerant coordination
7	Multi-master replication	Vector clocks, conflict resolution, geo-distributed sync
8	Burst scaling	10-50x capacity scaling for traffic spikes
9	Auto-sharding	Automatic data partitioning across nodes

AI & Machine Learning

#	Capability	What It Does
10	Run LLMs locally	ruvllm with GGUF, Metal/CUDA/ANE acceleration
11	RuvLTRA models	Pre-trained GGUF for routing & embeddings (<10ms) → HuggingFace
12	SONA learning	Self-Optimizing Neural Architecture with LoRA, EWC++
13	39 attention mechanisms	Flash, linear, graph, hyperbolic, mincut-gated (50% compute)
14	Spiking neural networks	Event-driven neuromorphic computing
15	Mincut-gated transformer	Dynamic attention via graph min-cut optimization
16	Route AI requests	Semantic routing + FastGRNN for LLM optimization

Specialized Processing

#	Capability	What It Does
17	SciPix OCR	LaTeX/MathML extraction from scientific documents
18	DAG workflows	Self-learning directed acyclic graph execution
19	Cognitum Gate	Cognitive AI gateway with TileZero acceleration
20	FPGA transformer	Hardware-accelerated transformer inference
21	Quantum coherence	ruQu for quantum error correction via dynamic min-cut

Platform & Integration

#	Capability	What It Does
22	Run anywhere	Node.js, browser (WASM), edge (rvLite), HTTP server, Rust
23	Drop into Postgres	pgvector-compatible extension with SIMD acceleration
24	MCP integration	Model Context Protocol server for AI assistant tools
25	Cloud deployment	One-click deploy to Cloud Run, Kubernetes

Self-Learning & Adaptation

#	Capability	What It Does
26	Self-learning hooks	Q-learning, neural patterns, HNSW memory
27	ReasoningBank	Trajectory learning with verdict judgment
28	Economy system	Tokenomics, CRDT-based distributed state
29	Nervous system	Event-driven reactive architecture
30	Agentic synthesis	Multi-agent workflow composition

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

---

Ecosystem: AI Agent Orchestration

RuVector powers two major AI orchestration platforms:

Platform	Purpose	Install
Claude-Flow	Enterprise multi-agent orchestration for Claude Code	npx @claude-flow/cli@latest
Agentic-Flow	Standalone AI agent framework (any LLM provider)	npx agentic-flow@latest

Claude-Flow v3 — Turn Claude Code into a collaborative AI team

54+ specialized agents working together on complex software engineering tasks:

# Install
npx @claude-flow/cli@latest init --wizard

# Spawn a swarm
npx @claude-flow/cli@latest swarm init --topology hierarchical --max-agents 8

Key Features:

• SONA Learning: Sub-50ms adaptive routing, learns optimal patterns over time
• Queen-led Swarms: Byzantine fault-tolerant consensus with 5 protocols (Raft, Gossip, CRDT)
• HNSW Memory: 150x-12,500x faster pattern retrieval via RuVector
• 175+ MCP Tools: Native Model Context Protocol integration
• Cost Optimization: 3-tier routing extends Claude Code quota by 2.5x
• Security: AIDefence threat detection (<10ms), prompt injection blocking

Agentic-Flow v2 — Production AI agents for any cloud

66 self-learning agents with Claude Agent SDK, deployable to any cloud:

# Install
npx agentic-flow@latest

# Or with npm
npm install agentic-flow

Key Features:

• SONA Architecture: <1ms adaptive learning, +55% quality improvement
• Flash Attention: 2.49x JS speedup, 7.47x with NAPI bindings
• 213 MCP Tools: Swarm management, memory, GitHub integration
• Agent Booster: 352x faster code editing for simple transforms
• Multi-Provider: Claude, GPT, Gemini, Cohere, local models with failover
• Graph Reasoning: GNN query refinement with +12.4% recall improvement

---

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

# Interactive installer - lists all packages
npx ruvector install

# Or install directly
npm install ruvector
npx ruvector

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

# LLM runtime (SONA learning, HNSW memory)
npm install @ruvector/ruvllm

Node.js / Browser

# Install
npm install ruvector

# Or try instantly
npx ruvector

📊 Comparison with Other Vector Databases

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	✅ 40 types	❌	❌	❌	❌
Coherence Gate	✅ Prime-Radiant	❌	❌	❌	❌
Hyperbolic Embeddings	✅ Poincaré+Lorentz	❌	❌	❌	❌
Local Embeddings	✅ 8+ 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	✅ WebGPU	❌	❌	❌	❌
Standalone Edge DB	✅ rvLite	❌	❌	❌	❌
LLM Runtime	✅ ruvllm	❌	❌	❌	❌
Pre-trained Models	✅ RuvLTRA (HF)	❌	❌	❌	❌
MCP Server	✅ mcp-gate	❌	❌	❌	❌
Self-Learning Hooks	✅ Q-learning+Neural+HNSW	❌	❌	❌	❌
Quantum Coherence	✅ ruQu	❌	❌	❌	❌
MinCut-Gated Attention	✅ 50% compute	❌	❌	❌	❌
FPGA Acceleration	✅	❌	❌	❌	❌
Local ONNX Embeddings	✅ 8+ models	❌	❌	❌	❌
Differentiable	✅	❌	❌	❌	❌
Multi-Tenancy	✅ Collections	✅	✅	✅	✅
DAG Workflows	✅ Self-learning	❌	❌	❌	❌
ReasoningBank	✅ Trajectory learning	❌	❌	❌	❌
Economy System	✅ CRDT tokenomics	❌	❌	❌	❌
Nervous System	✅ Event-driven	❌	❌	❌	❌
Cognitum Gate	✅ TileZero	❌	❌	❌	❌
SciPix OCR	✅ LaTeX/MathML	❌	❌	❌	❌
Spiking Neural Nets	✅ Neuromorphic	❌	❌	❌	❌
Node.js Native	✅ napi-rs	❌	❌	❌	✅
Burst Scaling	✅ 10-50x	✅	❌	✅	❌
Streaming API	✅	✅	✅	✅	❌
Open Source	✅ MIT	❌	✅	✅	✅

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

⚡ Core Features & Capabilities

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
Hyperbolic HNSW	Poincaré ball indexing for hierarchies	Better tree/taxonomy embeddings
Sparse Vectors	BM25/TF-IDF hybrid search	Combine keyword + semantic

LLM Runtime

Feature	What It Does	Why It Matters
ruvllm	Local LLM inference with GGUF models	Run AI without cloud APIs
Metal/CUDA/ANE	Hardware acceleration on Mac/NVIDIA/Apple	10-50x faster inference
ruvllm-wasm	Browser LLM with WebGPU acceleration	Client-side AI, zero latency
RuvLTRA Models	Pre-trained GGUF for routing & embeddings	<10ms inference → HuggingFace
Streaming Tokens	Real-time token generation	Responsive chat UX
Quantization	Q4, Q5, Q8 model support	Run 7B models in 4GB RAM

npm install @ruvector/ruvllm        # Node.js
cargo add ruvllm                    # Rust

Platform & Edge

Feature	What It Does	Why It Matters
rvLite	Standalone 2MB edge database	IoT, mobile, embedded
PostgreSQL Extension	77+ SQL functions, pgvector replacement	Drop-in upgrade for existing DBs
MCP Server	Model Context Protocol integration	AI assistant tool calling
WASM/Browser	Full client-side vector search	Offline-first apps
Node.js Bindings	Native napi-rs, zero-copy	No serialization overhead
HTTP/gRPC Server	REST API with streaming	Easy microservice integration

docker pull ruvnet/ruvector-postgres    # PostgreSQL
npm install rvlite                       # Edge DB
npx ruvector mcp start                   # MCP Server

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
Burst Scaling	10-50x capacity for traffic spikes	Handle viral moments

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
Hybrid Routing	Keyword-first + embedding fallback	90% accuracy for agent routing
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
Local Embeddings	8+ ONNX models built-in	No external API needed

Specialized Processing

Feature	What It Does	Why It Matters
SciPix OCR	LaTeX/MathML from scientific docs	Index research papers
DAG Workflows	Self-learning directed acyclic graphs	Complex pipeline orchestration
Cognitum Gate	Cognitive AI gateway + TileZero	Unified AI model routing
FPGA Transformer	Hardware-accelerated inference	Ultra-low latency serving
ruQu Quantum	Quantum error correction via min-cut	Future-proof algorithms
Mincut-Gated Transformer	Dynamic attention via graph optimization	50% compute reduction
Sparse Inference	Efficient sparse matrix operations	10x faster for sparse data

Self-Learning & Adaptation

Feature	What It Does	Why It Matters
Self-Learning Hooks	Q-learning + neural patterns + HNSW	System improves automatically
ReasoningBank	Trajectory learning with verdict judgment	Learn from successes/failures
Economy System	Tokenomics, CRDT-based distributed state	Incentivize agent behavior
Nervous System	Event-driven reactive architecture	Real-time adaptation
Agentic Synthesis	Multi-agent workflow composition	Emergent problem solving
EWC++	Elastic weight consolidation	Prevent catastrophic forgetting

npx @ruvector/cli hooks init      # Install self-learning hooks
npx @ruvector/cli hooks install   # Configure for Claude Code

Attention Mechanisms (@ruvector/attention)

Feature	What It Does	Why It Matters
40 Mechanisms	Dot-product, multi-head, flash, linear, sparse, cross-attention, CGT sheaf	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)
CGTSheafAttention	Coherence	Consistency-gated graph transformers

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]"

Coherence Gate (prime-radiant)

Feature	What It Does	Why It Matters
Sheaf Laplacian	Measures consistency via E(S) = Σ wₑ · ‖ρᵤ(xᵤ) - ρᵥ(xᵥ)‖²	Mathematical proof of coherence
Compute Ladder	Reflex (<1ms) → Retrieval (~10ms) → Heavy (~100ms) → Human	Route by confidence level
LLM Hallucination Gate	Block incoherent responses with witnesses	Refuse generation when math says contradiction
GPU/SIMD Acceleration	wgpu + AVX-512/NEON + vec4 WGSL kernels	4-16x speedup on coherence checks
Governance Audit	Blake3 hash chain, cryptographic witnesses	Every decision is provable

Coherence vs Confidence

Traditional AI	Prime-Radiant
"I'm 85% confident"	"Zero contradictions found"
Can be confidently wrong	Knows when it doesn't know
Guesses about the future	Proves consistency right now
Trust the model	Trust the math

Compute Ladder Routing

Energy	Lane	Latency	Action
< 0.1	Reflex	< 1ms	Immediate approval
0.1-0.4	Retrieval	~10ms	Fetch more evidence
0.4-0.7	Heavy	~100ms	Deep analysis
> 0.7	Human	async	Escalate to review

# Install coherence engine
cargo add prime-radiant

# With GPU acceleration
cargo add prime-radiant --features gpu,simd

🚀 Deployment Options

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

📈 Performance Benchmarks

Measured results from /bench_results/:

Configuration	QPS	p50 Latency	p99 Latency	Recall
ruvector (optimized)	1,216	0.78ms	0.78ms	100%
Multi-threaded (16)	3,597	2.86ms	8.47ms	100%
ef_search=50	674	1.35ms	1.35ms	100%
Python baseline	77	11.88ms	11.88ms	100%
Brute force	12	77.76ms	77.76ms	100%

Dataset: 384D, 10K-50K vectors. See full results in latency_benchmark.md.

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

🗜️ Adaptive 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

AI & LLM Applications

Use Case	Features Used	Example
RAG Pipelines	Vector search, Local embeddings, ruvllm	examples/ruvLLM
AI Agent Routing	Tiny Dancer, Semantic router, SONA	Claude-Flow
Multi-Agent Orchestration	GNN, HNSW memory, Consensus	Agentic-Flow
Self-Learning Chatbots	ReasoningBank, EWC++, Neural patterns	examples/meta-cognition

// RAG with local LLM (zero cloud costs)
import { RuVector } from 'ruvector';
import { RuvLLM } from '@ruvector/ruvllm';

const db = new RuVector({ dimensions: 384 });
const llm = new RuvLLM({ model: 'ruvltra-small-0.5b-q4_k_m.gguf' });

// Search learns from usage via GNN layers
const context = await db.search(questionEmbedding, { k: 5 });
const response = await llm.generate(`Context: ${context}\n\nQ: ${question}`);

Search & Discovery

Use Case	Features Used	Example
Semantic Search	HNSW, Metadata filtering, SIMD	Core feature
Hybrid Search	BM25 + embeddings, Sparse vectors	docs/api
Image Similarity	CLIP embeddings, Hyperbolic HNSW	examples/wasm-react
Code Search	Local ONNX embeddings, Graph queries	examples/nodejs

// Hybrid search: keyword + semantic
const results = await db.search(query, {
  k: 10,
  filter: { category: 'electronics', price: { $lt: 500 } },
  hybridAlpha: 0.7,  // 70% semantic, 30% keyword
  rerank: true       // GNN-enhanced reranking
});

Recommendations & Personalization

Use Case	Features Used	Example
Product Recommendations	Graph queries, Cypher, GNN	examples/graph
Content Personalization	User embeddings, Collaborative filtering	Real-time adaptation
Similar Items	Cosine similarity, Hyperbolic space	Hierarchical taxonomies

// Neo4j-style recommendations with learning
MATCH (user:User {id: $userId})-[:VIEWED]->(item:Product)
MATCH (item)-[:SIMILAR_TO]->(rec:Product)
WHERE NOT (user)-[:PURCHASED]->(rec)
RETURN rec ORDER BY rec.gnn_score DESC LIMIT 10

Knowledge Management

Use Case	Features Used	Example
Knowledge Graphs	Hypergraph, Cypher, SPARQL	docs/api/CYPHER_REFERENCE.md
Document Q&A	Chunking, Embeddings, RAG	examples/refrag-pipeline
Scientific Papers	SciPix OCR, LaTeX extraction	examples/scipix
Research Discovery	Citation graphs, Concept linking	Hyperedge relationships

// Multi-hop knowledge graph traversal
MATCH (paper:Paper)-[:CITES*1..3]->(cited:Paper)
WHERE paper.topic = 'machine learning'
MATCH (cited)-[:AUTHORED_BY]->(author:Researcher)
RETURN author, count(cited) as influence
ORDER BY influence DESC LIMIT 20

Real-Time & Edge Computing

Use Case	Features Used	Example
Browser AI	WASM, WebGPU, ruvllm-wasm	examples/wasm-vanilla
IoT Sensors	rvLite, Edge DB, no_std	examples/edge
Mobile Apps	2MB footprint, Offline-first	examples/edge-net
Streaming Data	Real-time indexing, Dynamic min-cut	examples/neural-trader

// Browser-based AI (no server required)
import init, { RuvLLMWasm } from '@ruvector/ruvllm-wasm';

await init();
const llm = await RuvLLMWasm.new(true); // WebGPU enabled
await llm.load_model_from_url('https://cdn.example.com/model.gguf');

// Runs entirely in browser
const response = await llm.generate('Explain quantum computing', {
  max_tokens: 200,
  temperature: 0.7
});

Scientific & Research

Use Case	Features Used	Example
Neural Network Analysis	Spiking NN, Meta-cognition	examples/meta-cognition
Algorithmic Trading	Neural Trader, Time-series	examples/neural-trader
Quantum Computing	ruQu, Min-cut coherence	crates/ruQu
Brain Connectivity	Dynamic min-cut, Network analysis	examples/mincut

// Neuromorphic computing with spiking networks
use ruvector_nervous_system::{SpikingNetwork, LIFNeuron};

let mut network = SpikingNetwork::new();
network.add_layer(LIFNeuron::new(128));  // 128 spiking neurons
network.enable_stdp();                    // Spike-timing plasticity

// 10-50x more energy efficient than traditional ANNs
let output = network.forward(&input_spikes);

Neuromorphic Computing (micro-hnsw v2.3)

Novel neuromorphic discoveries for brain-inspired vector search in 11.8KB WASM.

Discovery	Description	Benefit
Spike-Timing Vector Encoding	Convert vectors to temporal spike patterns	Temporal similarity matching
Homeostatic Plasticity	Self-stabilizing network activity	Prevents runaway activation
Oscillatory Resonance	Gamma-frequency (40Hz) search amplification	Improved recall via resonance
Winner-Take-All Circuits	Competitive neural selection with lateral inhibition	Sparse, efficient representations
Dendritic Computation	Non-linear local processing in dendrites	Complex pattern detection
STDP Learning	Spike-Timing Dependent Plasticity	Unsupervised Hebbian learning

// micro-hnsw: Neuromorphic HNSW in 11.8KB WASM
use micro_hnsw_wasm::{MicroHnsw, LIFNeuron, SpikeTrain};

// 256 cores × 32 vectors = 8K total capacity
let mut hnsw = MicroHnsw::new(16, Metric::Cosine);  // 16-dim vectors

// Spike-timing vector encoding
let spike_train = SpikeTrain::encode(&embedding, 8);  // 8-bit temporal resolution

// LIF neuron with STDP learning
let neuron = LIFNeuron::new(0.8);  // threshold = 0.8
neuron.enable_stdp(0.01, 0.012);   // A+ = 0.01, A- = 0.012
neuron.enable_homeostasis(0.1);    // Target rate: 0.1 spikes/ms

// Winner-take-all search with lateral inhibition
let results = hnsw.search_wta(&query, 10, 0.8);  // WTA inhibition = 0.8

Distributed & Enterprise

Use Case	Features Used	Example
Multi-Region Deployment	Raft consensus, Replication	docs/cloud-architecture
High Availability	Auto-sharding, Failover	99.99% SLA capable
PostgreSQL Integration	230+ SQL functions, pgvector replacement	crates/ruvector-postgres
Burst Traffic	10-50x scaling, Load balancing	examples/google-cloud

-- PostgreSQL with RuVector extension
CREATE EXTENSION ruvector;

-- Create vector column with GNN-enhanced index
CREATE TABLE documents (
  id SERIAL PRIMARY KEY,
  content TEXT,
  embedding VECTOR(384)
);

CREATE INDEX ON documents USING hnsw_gnn (embedding);

-- Self-improving search
SELECT * FROM documents
ORDER BY embedding <-> query_vector
LIMIT 10;

AI Safety & Coherence (Cognitum Gate)

A 256-tile WASM fabric for real-time AI agent safety decisions with cryptographic verification.

Component	Description	Memory
Worker Tiles (255)	Local graph shards, evidence accumulation, witness fragments	64KB each
TileZero Arbiter	Supergraph merging, global decisions, permit tokens	Central
Gate Decisions	Permit / Defer / Deny with confidence scores	<1ms
Witness Receipts	Hash-chained cryptographic audit trail	Immutable

Feature	Description
Anytime-Valid Testing	Sequential hypothesis testing with e-values
Min-Cut Aggregation	Global coherence via distributed min-cut
Signed Permits	Cryptographic tokens for approved actions
Evidence Filters	Three-filter decision system (structural, evidence, combined)

// Cognitum Gate: AI agent safety in microseconds
use cognitum_gate_tilezero::{GateDecision, ActionContext, PermitToken};

let gate = CoherenceGate::new_256_tiles();

// Evaluate action safety
let context = ActionContext {
    action_id: "deploy-model-v2".into(),
    action_type: "config_change".into(),
    agent_id: "coder-agent-01".into(),
    ..Default::default()
};

let decision = gate.evaluate(&context).await?;

match decision {
    GateDecision::Permit(token) => {
        // Cryptographically signed permit token
        assert!(token.verify(&gate.public_key()));
        execute_action(token);
    }
    GateDecision::Defer(reason) => {
        // Needs more evidence - retry later
        log::info!("Deferred: {}", reason);
    }
    GateDecision::Deny(evidence) => {
        // Action blocked with witness receipt
        log::warn!("Denied: {:?}", evidence);
    }
}

// Browser: Real-time safety checks via WASM
import { CognitumGate } from '@cognitum/gate';

const gate = await CognitumGate.init();

// Check action in <1ms
const result = await gate.evaluate({
  action: 'modify_user_data',
  agent: 'assistant-v3',
  context: { user_id: '12345' }
});

if (result.permitted) {
  const receipt = result.witnessReceipt;  // Hash-chained audit log
  console.log('Permit token:', result.token);
}

Dynamic Embedding Fine-Tuning

Use Case	Features Used	Example
Real-Time Adaptation	MicroLoRA (<1ms), Per-request learning	docs/ruvllm/FINE_TUNING.md
Contrastive Training	Triplet loss, Hard negatives, InfoNCE	npm/packages/ruvllm
Task-Specific Adapters	5 pre-defined adapters (Coder, Researcher, Security, Architect, Reviewer)	docs/training
Catastrophic Forgetting Prevention	EWC++ (Elastic Weight Consolidation)	crates/sona
Browser Fine-Tuning	MicroLoRA WASM, <50KB adapters	crates/ruvllm-wasm

Three-Tier Adaptation System:

Tier	Technique	Latency	Use Case
Instant	MicroLoRA (rank 1-2)	<1ms	Per-request adaptation
Background	Adapter Merge + EWC++	~100ms	Pattern consolidation
Deep	Full Training Pipeline	Minutes	Periodic optimization

// Real-time embedding fine-tuning with contrastive learning
import { ContrastiveTrainer, tripletLoss } from '@ruvector/ruvllm';

const trainer = new ContrastiveTrainer({
  epochs: 10,
  batchSize: 16,
  margin: 0.5,           // Triplet loss margin
  hardNegativeRatio: 0.7 // 70% hard negatives for better learning
});

// Train with triplets: anchor (task) → positive (correct agent) → negative (wrong agent)
trainer.addTriplet(taskEmb, correctAgentEmb, wrongAgentEmb, isHardNegative);
const results = trainer.train();
trainer.exportTrainingData('./fine-tuned-model');

// Rust: MicroLoRA for per-request adaptation
use ruvllm::lora::{MicroLoRA, MicroLoraConfig, AdaptFeedback};

let lora = MicroLoRA::new(MicroLoraConfig::for_hidden_dim(4096));

// During inference: apply LoRA delta
let output = model.forward(&input)?;
let delta = lora.forward(&input, &TargetModule::QProj);
let enhanced = output.iter().zip(delta.iter()).map(|(o, d)| o + d).collect();

// After response: adapt based on quality feedback
lora.adapt(&input, AdaptFeedback::from_quality(0.85))?;
lora.apply_updates(0.01); // Learning rate

// Browser: Real-time fine-tuning with MicroLoRA WASM
import init, { MicroLoraWasm, MicroLoraConfigWasm } from 'ruvllm-wasm';

await init();
const config = new MicroLoraConfigWasm();
config.rank = 2;           // Tiny rank for browser (<50KB)
config.alpha = 4.0;
config.inFeatures = 768;

const lora = new MicroLoraWasm(config);
const delta = lora.forward(hiddenStates);  // <1ms latency

// Persist to localStorage/IndexedDB
const json = lora.toJson();
localStorage.setItem('user-adapter', json);

Agentic Workflows

Use Case	Features Used	Example
Version Control for AI	Agentic Jujutsu, Branching	examples/agentic-jujutsu
Data Pipelines	DAG workflows, Self-learning	crates/ruvector-dag
Web Scraping	Apify integration, Embeddings	examples/apify
Synthetic Data	Agentic synthesis, Generation	Agentic-Flow

// Self-learning DAG workflow
import { QueryDag, AttentionSelector } from '@ruvector/dag';

const dag = new QueryDag();
dag.addNode({ type: 'fetch', source: 'api' });
dag.addNode({ type: 'embed', model: 'local-onnx' });
dag.addNode({ type: 'index', engine: 'hnsw' });

// DAG learns optimal execution paths over time
dag.enableSonaLearning();
await dag.execute();

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
Rust (LLM)	cargo add ruvllm

---

Package Reference

📖 Documentation

Getting Started

Topic	Link
Getting Started	docs/guides/GETTING_STARTED.md
API Reference	docs/api/
Cypher Reference	docs/api/CYPHER_REFERENCE.md
Performance Tuning	docs/optimization/PERFORMANCE_TUNING_GUIDE.md

Core Components

Topic	Link
GNN Architecture	docs/gnn/
HNSW Indexing	docs/hnsw/
DAG System	docs/dag/
Nervous System	docs/nervous-system/
Sparse Inference	docs/sparse-inference/

Bindings & Integration

Topic	Link
Node.js API	crates/ruvector-gnn-node/README.md
WASM API	crates/ruvector-gnn-wasm/README.md
PostgreSQL	docs/postgres/
Self-Learning Hooks	docs/hooks/
Integration Guides	docs/integration/

LLM & AI

Topic	Link
RuvLLM	docs/ruvllm/
Training Guides	docs/training/

Operations

Topic	Link
Architecture	docs/architecture/
Cloud Deployment	docs/cloud-architecture/
Security	docs/security/
Benchmarks	docs/benchmarks/
Testing	docs/testing/

Research

Topic	Link
Research Papers	docs/research/
GNN V2 Features	docs/research/gnn-v2/
Min-Cut Algorithms	docs/research/mincut/
SPARQL Support	docs/research/sparql/
Latent Space	docs/research/latent-space/

Architecture Decision Records (ADRs)

ADR	Status	Description
ADR-001	Accepted	Core architecture design
ADR-002	Accepted	RuvLLM integration
ADR-003	Accepted	SIMD optimization strategy
ADR-004	Accepted	KV cache management
ADR-005	Accepted	WASM runtime integration
ADR-006	Accepted	Memory management
ADR-007	Accepted	Security review
ADR-008	New	Mistral-rs backend integration
ADR-009	New	Structured output (SOTA)
ADR-010	New	Function calling (SOTA)
ADR-011	New	Prefix caching (SOTA)
ADR-012	New	Security remediation
ADR-013	New	HuggingFace publishing

📦 npm Packages (45+ Packages)

Core Packages

Package	Description	Version	Downloads
ruvector	All-in-one CLI & package
@ruvector/core	Core vector database with HNSW
@ruvector/node	Unified Node.js bindings
ruvector-extensions	Advanced features: embeddings, UI

Graph & GNN

Package	Description	Version	Downloads
@ruvector/gnn	Graph Neural Network layers
@ruvector/graph-node	Hypergraph with Cypher queries
@ruvector/graph-wasm	Browser graph queries
@ruvector/graph-data-generator	AI-powered synthetic graph data

AI Routing & Attention

Package	Description	Version	Downloads
@ruvector/tiny-dancer	FastGRNN neural routing
@ruvector/router	Semantic router + HNSW
@ruvector/attention	39 attention mechanisms

Learning & Neural

Package	Description	Version	Downloads
@ruvector/sona	Self-Optimizing Neural Architecture
@ruvector/spiking-neural	Spiking neural networks (SNN)

LLM Runtime

Package	Description	Version	Downloads
@ruvector/ruvllm	LLM orchestration + SONA
@ruvector/ruvllm-cli	LLM CLI: inference, benchmarks
@ruvector/ruvllm-wasm	Browser LLM inference

Distributed Systems

Package	Description	Version	Downloads
@ruvector/cluster	Distributed clustering
@ruvector/server	HTTP/gRPC server
@ruvector/raft	Raft consensus
@ruvector/replication	Multi-master replication
@ruvector/burst-scaling	10-50x burst scaling

Edge & Standalone

Package	Description	Version	Downloads
rvlite	SQLite-style edge DB
@ruvector/rudag	Self-learning DAG

Agentic & Synthetic Data

Package	Description	Version	Downloads
@ruvector/agentic-synth	AI synthetic data generator
@ruvector/agentic-integration	Distributed agent coordination
@cognitum/gate	AI coherence gate

CLI Tools

Package	Description	Version	Downloads
@ruvector/cli	CLI + self-learning hooks
@ruvector/postgres-cli	PostgreSQL extension CLI
@ruvector/scipix	Scientific OCR client

WASM Packages

Package	Description	Version	Downloads
@ruvector/wasm	Unified WASM meta-package
@ruvector/wasm-unified	Unified TypeScript API
@ruvector/gnn-wasm	GNN WASM bindings
@ruvector/attention-wasm	Attention WASM bindings
@ruvector/attention-unified-wasm	All 39 attention mechanisms
@ruvector/tiny-dancer-wasm	AI routing WASM
@ruvector/router-wasm	Semantic router WASM
@ruvector/learning-wasm	Learning module WASM
@ruvector/economy-wasm	Tokenomics WASM
@ruvector/exotic-wasm	Exotic features WASM
@ruvector/nervous-system-wasm	Nervous system WASM
ruvector-attention-wasm	WASM attention (Flash, MoE, Hyperbolic, CGT Sheaf)

🦀 Rust Crates (63 Packages)

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
ruvector-node	Node.js bindings via NAPI-RS
ruvector-wasm	WASM bindings for browser/edge
ruvector-cli	CLI and MCP server
ruvector-bench	Benchmarking suite

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

LLM Runtime (ruvllm)

Crate	Description	crates.io
ruvllm	LLM serving runtime with SONA, paged attention, KV cache
ruvllm-cli	CLI for model inference and benchmarking
ruvllm-wasm	WASM bindings for browser LLM inference

Features: Candle backend, Metal/CUDA acceleration, Apple Neural Engine, GGUF support, SONA learning integration.

cargo add ruvllm --features inference-metal  # Mac with Metal
cargo add ruvllm --features inference-cuda   # NVIDIA GPU

RuvLTRA Models — Pre-trained GGUF models optimized for Claude Code workflows:

Model	Size	Use Case	Link
ruvltra-claude-code-0.5b-q4_k_m	398 MB	Agent routing	HuggingFace
ruvltra-small-0.5b-q4_k_m	398 MB	Embeddings	HuggingFace
ruvltra-medium-1.1b-q4_k_m	800 MB	Classification	HuggingFace

# Download and use
wget https://huggingface.co/ruv/ruvltra/resolve/main/ruvltra-small-0.5b-q4_k_m.gguf

Distributed Systems

Crate	Description	crates.io
ruvector-cluster	Cluster management and coordination
ruvector-raft	Raft consensus implementation
ruvector-replication	Data replication and synchronization
ruvector-server	REST/gRPC API server

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

Hybrid Routing achieves 90% accuracy for agent routing using keyword-first strategy with embedding fallback. See Issue #122 for benchmarks and the training tutorials for fine-tuning guides.

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

Quantum Coherence (ruQu)

Crate	Description	crates.io
ruqu	Classical nervous system for quantum machines - coherence via min-cut
cognitum-gate-kernel	Anytime-valid coherence gate kernel
cognitum-gate-tilezero	TileZero arbiter for coherence decisions
mcp-gate	MCP server for coherence gate integration
prime-radiant	Universal coherence engine - sheaf Laplacian AI safety & hallucination detection

ruQu Features: Real-time quantum coherence assessment, MWPM decoder integration, mincut-gated attention (50% FLOPs reduction).

use ruqu::{CoherenceGate, SyndromeFilter};

let gate = CoherenceGate::new();
let syndrome = gate.assess_coherence(&quantum_state)?;

Advanced Math & Inference

Crate	Description	crates.io
ruvector-math	Core math utilities, SIMD operations
ruvector-math-wasm	WASM bindings for math operations
ruvector-sparse-inference	Sparse tensor inference engine
ruvector-sparse-inference-wasm	WASM bindings for sparse inference
ruvector-hyperbolic-hnsw	HNSW in hyperbolic space (Poincaré/Lorentz)
ruvector-hyperbolic-hnsw-wasm	WASM bindings for hyperbolic HNSW

FPGA & Hardware Acceleration

Crate	Description	crates.io
ruvector-fpga-transformer	FPGA-optimized transformer inference
ruvector-fpga-transformer-wasm	WASM simulation of FPGA transformer
ruvector-mincut-gated-transformer	MinCut-gated attention for 50% compute reduction
ruvector-mincut-gated-transformer-wasm	WASM bindings for mincut-gated transformer

Neuromorphic & Bio-Inspired Learning

Crate	Description	crates.io
ruvector-nervous-system	Spiking neural networks with BTSP learning & EWC plasticity
ruvector-nervous-system-wasm	WASM bindings for neuromorphic learning
ruvector-learning-wasm	MicroLoRA adaptation (<100µs latency)
ruvector-economy-wasm	CRDT-based autonomous credit economy
ruvector-exotic-wasm	Exotic AI primitives (strange loops, time crystals)
ruvector-attention-unified-wasm	Unified 18+ attention mechanisms (Neural, DAG, Mamba SSM)
micro-hnsw-wasm	Neuromorphic HNSW with spiking neurons (11.8KB WASM)

Bio-inspired features:

• Spiking Neural Networks (SNNs) — 10-50x energy efficiency vs traditional ANNs
• BTSP Learning — Behavioral Time-Scale Synaptic Plasticity for rapid adaptation
• MicroLoRA — Sub-microsecond fine-tuning for per-operator learning
• Mamba SSM — State Space Model attention for linear-time sequences

Self-Learning (SONA)

Crate	Description	crates.io
sona	Self-Optimizing Neural Architecture - LoRA, EWC++, ReasoningBank

SONA Features: Two-tier LoRA adaptation, Elastic Weight Consolidation (EWC++), ReasoningBank for trajectory learning, runtime-adaptive learning for LLM routers.

Standalone Edge Database (rvLite)

Crate	Description	crates.io
rvlite	Standalone 2MB edge database with SQL, SPARQL, Cypher

rvLite Features: Powered by RuVector WASM, supports SQL/SPARQL/Cypher queries, ideal for IoT, mobile, and embedded systems.

PostgreSQL Extension

Crate	Description	crates.io
ruvector-postgres	pgvector replacement with 230+ SQL functions, SIMD, Flash Attention

PostgreSQL Features: Drop-in pgvector replacement, GNN layers, hybrid search, multi-tenancy, self-healing, self-learning capabilities.

docker pull ruvnet/ruvector-postgres    # Docker image
cargo add ruvector-postgres             # Rust crate

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.

Distributed Systems (Raft & Replication)

Crate	Description	crates.io
ruvector-raft	Raft consensus with leader election & log replication
ruvector-replication	Multi-master replication with vector clocks
ruvector-cluster	Cluster coordination and sharding

Build distributed vector databases with strong consistency guarantees:

• Raft Consensus — Leader election, log replication, automatic failover
• Vector Clocks — Causal ordering for conflict detection
• Conflict Resolution — Last-Write-Wins, custom merge functions, CRDT support
• Change Data Capture — Stream changes to replicas in real-time
• Automatic Failover — Promote replicas on primary failure

import { RaftNode, ReplicaSet, VectorClock } from '@ruvector/raft';
import { ReplicationManager, ConflictStrategy } from '@ruvector/replication';

// Raft consensus cluster
const node = new RaftNode({
  nodeId: 'node-1',
  peers: ['node-2', 'node-3'],
  electionTimeout: [150, 300],
});

await node.start();
const entry = await node.propose({ op: 'insert', vector: embedding });

// Multi-master replication
const replicaSet = new ReplicaSet();
replicaSet.addReplica('primary', 'localhost:5001', 'primary');
replicaSet.addReplica('replica-1', 'localhost:5002', 'replica');

const manager = new ReplicationManager(replicaSet, {
  conflictStrategy: ConflictStrategy.LastWriteWins,
  syncMode: 'async',
});

await manager.write('vectors', { id: 'v1', data: embedding });

See npm/packages/raft/README.md and npm/packages/replication/README.md 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);

---

Platform Features

🔀 Self-Learning DAG (Query Optimization)

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

What is RuVector DAG?

A self-learning query optimization system—like a "nervous system" for your database queries that:

1. Watches how queries execute and identifies bottlenecks
2. Learns which optimization strategies work best for different query patterns
3. Adapts in real-time, switching strategies when conditions change
4. Heals itself by detecting anomalies and fixing problems before they impact users

Unlike traditional query optimizers that use static rules, RuVector DAG learns from actual execution patterns and gets smarter over time.

Key Benefits

Benefit	What It Does	Result
Automatic Improvement	Queries get faster without code changes	50-80% latency reduction after learning
Zero-Downtime Adaptation	Adapts to pattern changes automatically	No manual index rebuilds
Predictive Prevention	Detects rising "tension" early	Intervenes before slowdowns
Works Everywhere	PostgreSQL, Browser (58KB WASM), Embedded	Universal deployment

Use Cases

Use Case	Why RuVector DAG Helps
Vector Search Applications	Optimize similarity searches that traditional databases struggle with
High-Traffic APIs	Automatically adapt to changing query patterns throughout the day
Real-Time Analytics	Learn which aggregation paths are fastest for your specific data
Edge/Embedded Systems	58KB WASM build runs in browsers and IoT devices
Multi-Tenant Platforms	Learn per-tenant query patterns without manual tuning

How It Works

Query comes in → DAG analyzes execution plan → Best attention mechanism selected
                                                          ↓
Query executes → Results returned → Learning system records what worked
                                                          ↓
                    Next similar query benefits from learned optimizations

The system maintains a MinCut tension score that acts as a health indicator. When tension rises, the system automatically switches to more aggressive optimization strategies and triggers predictive healing.

7 DAG Attention Mechanisms

Mechanism	When to Use	Trigger
Topological	Default baseline	Low variance
Causal Cone	Downstream impact analysis	Write-heavy patterns
Critical Path	Latency-bound queries	p99 > 2x p50
MinCut Gated	Bottleneck-aware weighting	Cut tension rising
Hierarchical Lorentz	Deep hierarchical queries	Depth > 10
Parallel Branch	Wide parallel execution	Branch count > 3
Temporal BTSP	Time-series workloads	Temporal patterns

Quick Start

Rust:

use ruvector_dag::{QueryDag, OperatorNode, OperatorType};
use ruvector_dag::attention::{TopologicalAttention, DagAttention};

// Build a query DAG
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"));
let result = dag.add_node(OperatorNode::new(2, OperatorType::Result));

dag.add_edge(scan, filter).unwrap();
dag.add_edge(filter, result).unwrap();

// Compute attention scores
let attention = TopologicalAttention::new(Default::default());
let scores = attention.forward(&dag).unwrap();

Node.js:

import { QueryDag, TopologicalAttention } from '@ruvector/rudag';

// Build DAG
const dag = new QueryDag();
const scan = dag.addNode({ type: 'hnsw_scan', table: 'vectors', k: 64 });
const filter = dag.addNode({ type: 'filter', condition: 'score > 0.5' });
dag.addEdge(scan, filter);

// Apply attention
const attention = new TopologicalAttention();
const scores = attention.forward(dag);
console.log('Attention scores:', scores);

Browser (WASM - 58KB):

<script type="module">
import init, { QueryDag, TopologicalAttention } from '@ruvector/rudag-wasm';

await init();
const dag = new QueryDag();
// ... same API as Node.js
</script>

SONA Learning Integration

SONA (Self-Optimizing Neural Architecture) runs post-query in background, never blocking execution:

use ruvector_dag::sona::{DagSonaEngine, SonaConfig};

let config = SonaConfig {
    embedding_dim: 256,
    lora_rank: 2,           // Rank-2 for <100μs updates
    ewc_lambda: 5000.0,     // Catastrophic forgetting prevention
    trajectory_capacity: 10_000,
};
let mut sona = DagSonaEngine::new(config);

// Pre-query: Get enhanced embedding (fast path)
let enhanced = sona.pre_query(&dag);

// Execute query...
let execution_time = execute_query(&dag);

// Post-query: Record trajectory (async, background)
sona.post_query(&dag, execution_time, baseline_time, "topological");

Self-Healing

Reactive (Z-score anomaly detection) + Predictive (rising MinCut tension triggers early intervention):

use ruvector_dag::healing::{HealingOrchestrator, AnomalyConfig, PredictiveConfig};

let mut orchestrator = HealingOrchestrator::new();

// Reactive: Z-score anomaly detection
orchestrator.add_detector("query_latency", AnomalyConfig {
    z_threshold: 3.0,
    window_size: 100,
    min_samples: 10,
});

// Predictive: Rising cut tension triggers early intervention
orchestrator.enable_predictive(PredictiveConfig {
    tension_threshold: 0.6,    // Intervene before 0.7 crisis
    variance_threshold: 1.5,   // Rising variance = trouble coming
    lookahead_window: 50,      // Predict 50 queries ahead
});

Query Convergence Example

A slow query converges over several runs:

[run 1] query: SELECT * FROM vectors WHERE embedding <-> $1 < 0.5
        attention: topological (default)
        mincut_tension: 0.23
        latency: 847ms (improvement: 0.4%)

[run 4] mincut_tension: 0.71 > 0.7 (THRESHOLD)
        --> switching attention: topological -> mincut_gated
        latency: 412ms (improvement: 51.5%)

[run 10] attention: mincut_gated
         mincut_tension: 0.22 (stable)
         latency: 156ms (improvement: 81.6%)

Performance Targets

Component	Target	Notes
Attention (100 nodes)	<100μs	All 7 mechanisms
MicroLoRA adaptation	<100μs	Rank-2, per-operator
Pattern search (10K)	<2ms	K-means++ indexing
MinCut update	O(n^0.12)	Subpolynomial amortized
Anomaly detection	<50μs	Z-score, streaming
WASM size	58KB	Gzipped, browser-ready

Installation

# Rust
cargo add ruvector-dag

# Node.js
npm install @ruvector/rudag

# WASM (browser)
npm install @ruvector/rudag-wasm

Full Documentation: ruvector-dag README

📦 rvLite - Standalone Edge Database

A complete vector database that runs anywhere JavaScript runs — browsers, Node.js, Deno, Bun, Cloudflare Workers, Vercel Edge Functions.

What is rvLite?

rvLite is a lightweight, standalone vector database that runs entirely in WebAssembly. It provides SQL, SPARQL, and Cypher query interfaces, along with graph neural networks and self-learning capabilities—all in under 3MB.

Key Features

Feature	What It Does	Why It Matters
SQL Interface	Familiar SELECT, INSERT, WHERE	No learning curve
SPARQL Support	W3C-compliant RDF queries	Knowledge graphs in browser
Cypher Queries	Neo4j-style graph queries	Graph traversals anywhere
GNN Embeddings	Graph neural network layers	Self-learning search
ReasoningBank	Trajectory learning	Gets smarter over time
SIMD Optimized	Vector operations accelerated	Native-like performance

Runs Everywhere

Platform	Status	Use Case
Browsers	✅	Offline-first apps
Node.js	✅	Server-side
Deno	✅	Edge functions
Bun	✅	Fast runtime
Cloudflare Workers	✅	Edge computing
Vercel Edge	✅	Serverless

Architecture

┌─────────────────────────────────────────┐
│  RvLite (Orchestration)                 │
│  ├─ SQL executor                        │
│  ├─ SPARQL executor                     │
│  ├─ Cypher executor                     │
│  └─ Unified WASM API                    │
└──────────────┬──────────────────────────┘
               │ depends on (100% reuse)
               ▼
┌──────────────────────────────────────────┐
│  Existing WASM Crates                    │
├──────────────────────────────────────────┤
│  • ruvector-core (vectors, SIMD)         │
│  • ruvector-graph-wasm (Cypher)          │
│  • ruvector-gnn-wasm (GNN layers)        │
│  • sona (ReasoningBank learning)         │
│  • micro-hnsw-wasm (ultra-fast HNSW)     │
└──────────────────────────────────────────┘

Quick Start

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

// Create database
const db = await RvLite.create();

// SQL with vector search
await db.sql(`
  CREATE TABLE docs (
    id SERIAL PRIMARY KEY,
    content TEXT,
    embedding VECTOR(384)
  )
`);

await db.sql(`
  SELECT id, content, embedding <=> $1 AS distance
  FROM docs
  ORDER BY distance
  LIMIT 10
`, [queryVector]);

// Cypher graph queries
await db.cypher(`
  CREATE (a:Person {name: 'Alice'})-[:KNOWS]->(b:Person {name: 'Bob'})
`);

// SPARQL RDF queries
await db.sparql(`
  SELECT ?name WHERE {
    ?person foaf:name ?name .
  }
`);

// GNN embeddings
const embeddings = await db.gnn.computeEmbeddings('social_network', [
  db.gnn.createLayer('gcn', { inputDim: 128, outputDim: 64 })
]);

// Self-learning with ReasoningBank
await db.learning.recordTrajectory({ state: [0.1], action: 2, reward: 1.0 });
await db.learning.train({ algorithm: 'q-learning', iterations: 1000 });

Size Budget

Component	Size	Purpose
ruvector-core	~500KB	Vectors, SIMD
SQL parser	~200KB	Query parsing
SPARQL executor	~300KB	RDF queries
Cypher (graph-wasm)	~600KB	Graph queries
GNN layers	~300KB	Neural networks
ReasoningBank (sona)	~300KB	Self-learning
Total	~2.3MB	Gzipped

Installation

# npm
npm install @ruvector/rvlite

# Rust
cargo add rvlite

# Build WASM
wasm-pack build --target web --release

Full Documentation: rvlite README

🌐 Edge-Net - Collective AI Computing Network

Share, Contribute, Compute Together — A distributed computing platform that enables collective resource sharing for AI workloads.

What is Edge-Net?

Edge-Net creates a collective computing network where participants share idle browser resources to power distributed AI workloads:

┌─────────────────────────────────────────────────────────────────────────────┐
│              EDGE-NET: COLLECTIVE AI COMPUTING NETWORK                      │
├─────────────────────────────────────────────────────────────────────────────┤
│   ┌─────────────┐       ┌─────────────┐       ┌─────────────┐              │
│   │  Your       │       │  Collective │       │  AI Tasks   │              │
│   │  Browser    │◄─────►│  Network    │◄─────►│  Completed  │              │
│   │  (Idle CPU) │  P2P  │  (1000s)    │       │  for You    │              │
│   └─────────────┘       └─────────────┘       └─────────────┘              │
│         │                     │                     │                       │
│   Contribute ──────► Earn rUv Units ──────► Use for AI Workloads           │
└─────────────────────────────────────────────────────────────────────────────┘

How It Works

Step	What Happens	Result
1. Contribute	Share unused CPU cycles when browsing	Help the network
2. Earn	Accumulate rUv (Resource Utility Vouchers)	Build credits
3. Use	Spend rUv to run AI tasks	Access collective power
4. Network Grows	More participants = more power	Everyone benefits

Why Collective AI Computing?

Traditional AI	Collective Edge-Net
Expensive GPU servers	Free idle browser CPUs
Centralized data centers	Distributed global network
Pay-per-use pricing	Contribution-based access
Single point of failure	Resilient P2P mesh
Limited by your hardware	Scale with the collective

AI Intelligence Stack

┌─────────────────────────────────────────────────────────────────────────────┐
│                        AI INTELLIGENCE STACK                                 │
├─────────────────────────────────────────────────────────────────────────────┤
│  ┌─────────────────────────────────────────────────────────────────────┐   │
│  │                    MicroLoRA Adapter Pool (from ruvLLM)              │   │
│  │  • LRU-managed pool (16 slots) • <50µs rank-1 forward               │   │
│  │  • 4-bit/8-bit quantization    • 2,236+ ops/sec                     │   │
│  └─────────────────────────────────────────────────────────────────────┘   │
│  ┌─────────────────────────────────────────────────────────────────────┐   │
│  │                    SONA - Self-Optimizing Neural Architecture         │   │
│  │  • Instant Loop: Per-request MicroLoRA adaptation                    │   │
│  │  • Background Loop: Hourly K-means consolidation                     │   │
│  │  • Deep Loop: Weekly EWC++ (prevents catastrophic forgetting)        │   │
│  └─────────────────────────────────────────────────────────────────────┘   │
│  ┌──────────────────────┐  ┌──────────────────────┐  ┌─────────────────┐  │
│  │   HNSW Vector Index  │  │  Federated Learning  │  │ ReasoningBank   │  │
│  │   • 150x faster      │  │  • Byzantine tolerant│  │ • Pattern learn │  │
│  │   • O(log N) search  │  │  • Diff privacy      │  │ • 87x energy    │  │
│  └──────────────────────┘  └──────────────────────┘  └─────────────────┘  │
└─────────────────────────────────────────────────────────────────────────────┘

Core AI Tasks

Task Type	Use Case	Performance
Vector Search	Find similar items	150x speedup via HNSW
Embeddings	Text understanding	Semantic vectors
Semantic Match	Intent detection	Classify meaning
LoRA Inference	Task adaptation	<100µs forward
Pattern Learning	Self-optimization	ReasoningBank trajectories

Pi-Key Identity System

Ultra-compact cryptographic identity using mathematical constants:

Key Type	Size	Purpose
π (Pi-Key)	40 bytes	Permanent identity
e (Session)	34 bytes	Encrypted sessions
φ (Genesis)	21 bytes	Network origin markers

Self-Optimizing Features

Feature	Mechanism	Benefit
Task Routing	Multi-head attention	Work goes to best nodes
Topology Optimization	Self-organizing mesh	Network adapts to load
Q-Learning Security	Reinforcement learning	Learns to defend threats
Economic Balance	rUv token system	Self-sustaining economy

Quick Start

Add to Your Website:

<script type="module">
  import init, { EdgeNetNode, EdgeNetConfig } from '@ruvector/edge-net';

  async function joinCollective() {
    await init();

    // Join the collective
    const node = new EdgeNetConfig('my-website')
      .cpuLimit(0.3)          // Contribute 30% CPU when idle
      .memoryLimit(256 * 1024 * 1024)  // 256MB max
      .respectBattery(true)   // Reduce on battery
      .build();

    node.start();

    // Monitor participation
    setInterval(() => {
      console.log(`Contributed: ${node.ruvBalance()} rUv`);
    }, 10000);
  }

  joinCollective();
</script>

Use the Collective's AI Power:

// Submit an AI task to the collective
const result = await node.submitTask('vector_search', {
  query: embeddings,
  k: 10,
  index: 'shared-knowledge-base'
}, 5);  // Spend up to 5 rUv

console.log('Similar items:', result);

Monitor Your Contribution:

const stats = node.getStats();
console.log(`
  rUv Earned: ${stats.ruv_earned}
  rUv Spent: ${stats.ruv_spent}
  Tasks Completed: ${stats.tasks_completed}
  Reputation: ${(stats.reputation * 100).toFixed(1)}%
`);

Key Features

Feature	Benefit
Idle CPU Utilization	Use resources that would otherwise be wasted
Browser-Based	No installation, runs in any modern browser
Adjustable Contribution	Control how much you share (10-50% CPU)
Battery Aware	Automatically reduces on battery power
Fair Distribution	Work routed based on capability matching
Privacy-First	Pi-Key cryptographic identity
Federated Learning	Learn collectively without sharing data
Byzantine Tolerance	Resilient to malicious nodes

Installation

# npm
npm install @ruvector/edge-net

# Or include directly
<script src="https://unpkg.com/@ruvector/edge-net"></script>

Full Documentation: edge-net README

---

AI & Machine Learning

🎲 Agentic-Synth - AI Synthetic Data Generation

AI-Powered Synthetic Data Generation at Scale — Generate unlimited, high-quality synthetic data for training AI models, testing systems, and building robust agentic applications.

Why Agentic-Synth?

Problem	Solution
Real data is expensive to collect	Generate unlimited synthetic data
Privacy-sensitive with compliance risks	Fully synthetic, no PII concerns
Slow to generate at scale	10-100x faster than manual creation
Insufficient for edge cases	Customizable schemas for any scenario
Hard to reproduce across environments	Reproducible with seed values

Key Features

Feature	Description
Multi-Model Support	Gemini, OpenRouter, GPT, Claude, and 50+ models via DSPy.ts
Context Caching	95%+ performance improvement with intelligent LRU cache
Smart Model Routing	Automatic load balancing, failover, and cost optimization
DSPy.ts Integration	Self-learning optimization with 20-25% quality improvement
Streaming	AsyncGenerator for real-time data flow
Memory Efficient	<50MB for datasets up to 10K records

Data Generation Types

Type	Use Cases
Time-Series	Financial data, IoT sensors, metrics
Events	Logs, user actions, system events
Structured	JSON, CSV, databases, APIs
Embeddings	Vector data for RAG systems

Quick Start

# Install
npm install @ruvector/agentic-synth

# Or run instantly with npx
npx @ruvector/agentic-synth generate --count 100

# Interactive mode
npx @ruvector/agentic-synth interactive

Basic Usage

import { AgenticSynth } from '@ruvector/agentic-synth';

// Initialize with your preferred model
const synth = new AgenticSynth({
  model: 'gemini-pro',
  apiKey: process.env.GEMINI_API_KEY
});

// Generate structured data
const users = await synth.generate({
  schema: {
    name: 'string',
    email: 'email',
    age: 'number:18-65',
    role: ['admin', 'user', 'guest']
  },
  count: 1000
});

// Generate time-series data
const stockData = await synth.timeSeries({
  fields: ['open', 'high', 'low', 'close', 'volume'],
  interval: '1h',
  count: 500,
  volatility: 0.02
});

// Stream large datasets
for await (const batch of synth.stream({ count: 100000, batchSize: 1000 })) {
  await processData(batch);
}

Self-Learning with DSPy

import { AgenticSynth, DSPyOptimizer } from '@ruvector/agentic-synth';

// Enable self-learning optimization
const synth = new AgenticSynth({
  model: 'gemini-pro',
  optimizer: new DSPyOptimizer({
    learningRate: 0.1,
    qualityThreshold: 0.85
  })
});

// Quality improves automatically over time
const data = await synth.generate({
  schema: { ... },
  count: 1000,
  optimize: true  // Enable learning
});

console.log(`Quality score: ${data.metrics.quality}`);
// First run: 0.72
// After 100 runs: 0.94 (+25% improvement)

Performance

Metric	Value
With caching	98.2% faster
P99 latency	2500ms → 45ms
Memory	<50MB for 10K records
Throughput	1000+ records/sec

Ecosystem Integration

Package	Purpose
RuVector	Native vector database for RAG
DSPy.ts	Prompt optimization
Agentic-Jujutsu	Version-controlled generation

Installation

# npm
npm install @ruvector/agentic-synth

# Examples package (50+ production examples)
npm install @ruvector/agentic-synth-examples

Full Documentation: agentic-synth README

📈 Neural Trader - AI Trading System

Production-ready neural trading system combining state-of-the-art ML for automated trading, sports betting, and portfolio management. Zero external ML dependencies, sub-millisecond latency.

Core AI/ML Engines

Engine	Description	Performance
Fractional Kelly	Optimal position sizing with risk-adjusted bet scaling	588,885 ops/s
LSTM-Transformer	Deep learning price prediction (temporal + attention)	1,468 seq/s
DRL Portfolio	Reinforcement learning ensemble (PPO/SAC/A2C)	17,043 steps/s
Sentiment Alpha	Real-time sentiment analysis for alpha generation	3,764 pipeline/s

Why Neural Trader?

Traditional ML	Neural Trader
TensorFlow/PyTorch required	Zero dependencies
1.2MB+ bundle size	45KB bundle
2.1ms LSTM inference	0.68ms inference
Complex deployment	Works in browser & Node.js

Research-Backed Algorithms

Algorithm	Research Finding
Kelly Criterion	1/5th fractional achieves 98% ROI with 85% less risk of ruin
LSTM-Transformer	Temporal + attention fusion outperforms single architectures
DRL Ensemble	PPO/SAC/A2C voting reduces variance vs single agent
Sentiment Alpha	3% annual excess returns documented in academia

Quick Start

import { KellyCriterion, HybridLSTMTransformer, DRLPortfolioManager } from 'neural-trader';

// Kelly position sizing
const kelly = new KellyCriterion();
const stake = kelly.calculateStake(9000, 0.55, 2.0, 0.2);  // 1/5th Kelly
// → $180 recommended stake (2% of bankroll)

// LSTM-Transformer prediction
const model = new HybridLSTMTransformer({
  lstm: { hiddenSize: 64, layers: 2 },
  transformer: { heads: 4, layers: 2 }
});
const prediction = model.predict(candles);
// → { signal: 'BUY', confidence: 0.73, direction: 'bullish' }

// DRL portfolio allocation
const manager = new DRLPortfolioManager({ numAssets: 10 });
await manager.train(marketData, { episodes: 100 });
const allocation = manager.getAction(currentState);
// → [0.15, 0.12, 0.08, ...] optimal weights

Use Cases

Use Case	Example
Stock Trading	DAG-based pipeline with parallel execution
Sports Betting	Kelly sizing with ML calibration
Crypto Trading	DRL portfolio for 20+ assets
News Trading	Real-time sentiment stream processing
Portfolio Rebalancing	Reinforcement learning allocation

Package Ecosystem (20+)

Package	Description
neural-trader	Core engine with native HNSW, SIMD
@neural-trader/core	Ultra-low latency Rust + Node.js bindings
@neural-trader/strategies	Strategy management and backtesting
@neural-trader/execution	Trade execution and order management
@neural-trader/mcp	MCP server with 87+ trading tools
@neural-trader/risk	VaR, stress testing, risk metrics
@neural-trader/portfolio	Markowitz, Risk Parity optimization
@neural-trader/neural	Neural network training
@neural-trader/brokers	Alpaca, Interactive Brokers
@neural-trader/sports-betting	Arbitrage, Kelly, odds analysis

CLI Interface

# Real-time trading
node cli.js run --strategy=hybrid --symbol=AAPL --capital=100000

# Backtest historical performance
node cli.js backtest --days=252 --capital=50000 --strategy=drl

# Paper trading simulation
node cli.js paper --capital=100000 --strategy=sentiment

# Performance benchmarks
node cli.js benchmark --iterations=100

Exotic Examples

Example	Description
Multi-Agent Swarm	Distributed trading intelligence with consensus
GNN Correlation Network	Graph neural network correlation analysis
Attention Regime Detection	Transformer-based market regime classification
Quantum Portfolio	QAOA & quantum annealing optimization
Hyperbolic Embeddings	Poincaré disk market embeddings
Atomic Arbitrage	Cross-exchange with MEV protection

Performance

Module	Latency	Throughput	Status
Kelly Engine	0.014ms	71,295/s	✓ Ready
LSTM-Transformer	0.681ms	1,468/s	✓ Ready
DRL Portfolio	0.059ms	17,043/s	✓ Ready
Sentiment Alpha	0.266ms	3,764/s	✓ Ready
Full Pipeline	4.68ms	214/s	✓ Ready

Installation

# npm
npm install neural-trader

# Full ecosystem
npm install @neural-trader/core @neural-trader/strategies @neural-trader/mcp

Full Documentation: neural-trader README

🥋 Agentic-Jujutsu - Quantum-Resistant Version Control

What is Agentic-Jujutsu?

Agentic-Jujutsu is a quantum-resistant, self-learning version control system designed for AI agents. It combines lock-free concurrent operations with ReasoningBank trajectory learning for continuous improvement.

Traditional Git	Agentic-Jujutsu
Lock-based commits	Lock-free (23x faster)
Manual conflict resolution	87% automatic resolution
Static operations	Self-learning from patterns
No quantum protection	SHA3-512 + HQC-128
Sequential agents	Concurrent multi-agent

Key Features

Feature	Performance	Description
Concurrent Commits	350 ops/s	23x faster than Git (15 ops/s)
Context Switching	<100ms	10x faster than Git (500-1000ms)
Conflict Resolution	87% auto	AI-powered pattern matching
Quantum Security	<1ms verify	SHA3-512 fingerprints, HQC-128 encryption
ReasoningBank	Continuous	Trajectory learning with verdicts

Quick Start

# Install
npm install agentic-jujutsu

# Basic usage
npx agentic-jujutsu

import { JjWrapper } from 'agentic-jujutsu';

const jj = new JjWrapper();

// Start learning trajectory
jj.startTrajectory('Implement feature X');

// Make changes and commit
await jj.newCommit('Add authentication module');
jj.addToTrajectory();

// Finalize with success score
jj.finalizeTrajectory(0.9, 'Feature implemented successfully');

// Get AI-powered suggestions
const suggestions = await jj.getSuggestions();

Multi-Agent Coordination

// Concurrent commits without locks
const agents = ['agent-1', 'agent-2', 'agent-3'];
await Promise.all(agents.map(agent =>
  jj.newCommit(`Changes from ${agent}`)
));
// All commits succeed - no lock waiting!

Full Documentation: agentic-jujutsu README

🔬 SciPix - Scientific Document OCR

What is SciPix?

SciPix is a blazing-fast, memory-safe OCR engine written in pure Rust, purpose-built for scientific documents, mathematical equations, and technical diagrams.

Feature	SciPix	Tesseract	Mathpix
Simple Text OCR	50ms	120ms	200ms*
Math Equation	80ms	N/A	150ms*
Batch (100 images)	2.1s	8.5s	N/A
Memory Usage	45MB	180MB	Cloud
LaTeX Output	Yes	No	Yes

*API latency, not processing time

Key Features

Feature	Description
ONNX Runtime	GPU-accelerated with CUDA, TensorRT, CoreML
LaTeX Output	Mathematical equation recognition with LaTeX, MathML, AsciiMath
SIMD Optimized	4x faster image preprocessing with AVX2, SSE4, NEON
REST API	Production-ready HTTP server with rate limiting
MCP Server	Integrate with Claude, ChatGPT via Model Context Protocol
WebAssembly	Run OCR directly in browsers

Quick Start

# Add to Cargo.toml
cargo add ruvector-scipix

# With features
ruvector-scipix = { version = "0.1.16", features = ["ocr", "math", "optimize"] }

use ruvector_scipix::{SciPixOcr, OcrConfig};

// Initialize OCR engine
let ocr = SciPixOcr::new(OcrConfig::default())?;

// Process scientific image
let result = ocr.process_image("equation.png")?;
println!("LaTeX: {}", result.latex);
println!("Confidence: {:.2}%", result.confidence * 100.0);

Use Cases

• Academic Paper Digitization - Extract text and equations from scanned research papers
• Math Homework Assistance - Convert handwritten equations to LaTeX for AI tutoring
• Technical Documentation - Process engineering diagrams and scientific charts
• AI/LLM Integration - Feed scientific content to language models via MCP

Full Documentation: scipix README

🧠 Meta-Cognition SNN - Spiking Neural Networks

What is Meta-Cognition SNN?

A hybrid AI architecture combining Spiking Neural Networks (SNN), SIMD-optimized vector operations, and 5 attention mechanisms with meta-cognitive self-discovery capabilities.

Capability	Performance	Description
Spiking Neural Networks	10-50x faster	LIF neurons + STDP learning with N-API SIMD
SIMD Vector Operations	5-54x faster	Loop-unrolled distance/dot product calculations
5 Attention Mechanisms	Sub-millisecond	Multi-Head, Flash, Linear, Hyperbolic, MoE
Vector Search	150x faster	RuVector-powered semantic search
Meta-Cognition	Autonomous	Self-discovering emergent capabilities

SIMD Performance

Operation	Speedup	Notes
LIF Updates	16.7x	Leaky integrate-and-fire neurons
Synaptic Forward	14.9x	Forward propagation
STDP Learning	26.3x	Spike-timing dependent plasticity
Distance (128d)	54x	Euclidean distance calculation
Full Simulation	18.4x	End-to-end SNN simulation

5 Attention Mechanisms

Mechanism	Best For	Latency
Flash	Long sequences	0.023ms
MoE	Specialized domains	0.021ms
Multi-Head	Complex patterns	0.047ms
Linear	Real-time processing	0.075ms
Hyperbolic	Hierarchical data	0.222ms

Quick Start

# Install and run demos
cd examples/meta-cognition-spiking-neural-network
npm install
node demos/run-all.js

const { createFeedforwardSNN, rateEncoding } = require('./demos/snn/lib/SpikingNeuralNetwork');

// Create SNN with SIMD optimization
const snn = createFeedforwardSNN([100, 50, 10], {
  dt: 1.0,
  tau: 20.0,
  a_plus: 0.005,
  lateral_inhibition: true
});

// Train with STDP
const input = rateEncoding(pattern, snn.dt, 100);
snn.step(input);

6 Emergent Discoveries

1. Multi-Scale Attention Hierarchy (Novelty: 5/5)
2. Spike Synchronization Patterns
3. Attention-Gated Spike Propagation
4. Temporal Coherence Emergence
5. Emergent Sparsity (80% fewer active neurons)
6. Meta-Plasticity (faster learning on later tasks)

Full Documentation: meta-cognition-snn README

🤖 RuvLLM - Self-Learning LLM Orchestration

What is RuvLLM?

RuvLLM is a self-learning language model orchestration system that combines frozen foundation models with adaptive memory and intelligent routing. Unlike traditional LLMs that rely solely on static parameters, RuvLLM continuously improves from every interaction.

"The intelligence is not in one model anymore. It is in the loop."

SONA: 3-Tier Temporal Learning

Loop	Frequency	Latency	Description
A: Instant	Per-request	<100μs	MicroLoRA adaptation (rank 1-2)
B: Background	Hourly	~1.3ms	K-means++ clustering, base LoRA (rank 4-16)
C: Deep	Weekly	N/A	EWC++ consolidation, concept hierarchies

Core Components

Component	Description
LFM2 Cortex	Frozen reasoning engine (135M-2.6B params)
Ruvector Memory	Adaptive synaptic mesh with HNSW indexing
FastGRNN Router	Intelligent model selection circuit
Graph Attention	8-head attention with edge features
SONA Engine	LoRA + EWC++ + ReasoningBank

Performance (CPU-Only)

Metric	Value
Initialization	3.71ms
Average Query	0.09ms
Session Query	0.04ms
Throughput	~38,000 q/s
Memory	~50MB

Quick Start

use ruvllm::{RuvLLMOrchestrator, OrchestratorConfig};

// Initialize orchestrator
let config = OrchestratorConfig::default();
let orchestrator = RuvLLMOrchestrator::new(config)?;

// Query with automatic learning
let response = orchestrator.query("Explain quantum entanglement").await?;
println!("{}", response.text);

// Response improves over time as SONA learns patterns

Federated Learning

// Ephemeral agents collect trajectories
let agent = EphemeralAgent::new("task-specific-agent");
agent.process_task(&task).await?;
let export = agent.export();

// Central coordinator aggregates learning
coordinator.accept_export(export)?;
coordinator.consolidate();  // Share patterns with new agents

Dynamic Embedding Fine-Tuning

RuvLLM's adaptive learning system enables real-time model improvement without retraining.

Feature	Description	Latency
MicroLoRA	Per-request adaptation (rank 1-2), <50KB adapters	<1ms
Contrastive Training	Triplet loss with hard negatives for embedding optimization	Batch
Task-Specific Adapters	Pre-tuned for Coder, Researcher, Security, Architect, Reviewer	Hot-swap
EWC++ Consolidation	Prevents catastrophic forgetting during continuous learning	Background
Adapter Merging	Average, Weighted, SLERP, TIES, DARE strategies	On-demand

// Contrastive fine-tuning for agent routing
import { ContrastiveTrainer } from '@ruvector/ruvllm';

const trainer = new ContrastiveTrainer({
  margin: 0.5,
  hardNegativeRatio: 0.7
});

// Learn: task → correct agent, not wrong agent
trainer.addTriplet(taskEmb, correctAgentEmb, wrongAgentEmb, true);
const model = trainer.train();

// Task-specific adapter hot-swapping
use ruvllm::lora::RuvLtraAdapters;

let adapters = RuvLtraAdapters::new();
let coder = adapters.create_lora("coder", 768)?;      // Rank 16, code patterns
let security = adapters.create_lora("security", 768)?; // Rank 16, vulnerability detection

// Hot-swap at runtime without model reload
orchestrator.set_adapter(coder);
let code_response = orchestrator.query("Implement binary search").await?;

orchestrator.set_adapter(security);
let audit_response = orchestrator.query("Audit this code for vulnerabilities").await?;

Advanced Features

• SIMD Inference: AVX2/AVX512/SSE4.1 optimization
• Q4 Quantization: 4-bit weights for memory efficiency
• HuggingFace Export: Export LoRA weights and preference pairs
• Multi-Model Routing: SmolLM, Qwen2, TinyLlama selection
• WASM Support: Run SONA in browsers and edge devices
• Browser Fine-Tuning: MicroLoRA WASM with localStorage persistence

Full Documentation: ruvLLM README | Fine-Tuning Guide | Task Adapters

🗜️ REFRAG - Compress-Sense-Expand RAG

What is REFRAG?

REFRAG implements the Compress-Sense-Expand architecture from arXiv:2509.01092, achieving ~30x RAG latency reduction by storing pre-computed representation tensors instead of raw text.

Architecture

┌────────────────┐    ┌────────────────┐    ┌────────────────┐
│   COMPRESS     │───▶│     SENSE      │───▶│    EXPAND      │
│    Layer       │    │     Layer      │    │    Layer       │
└────────────────┘    └────────────────┘    └────────────────┘

Binary tensor         Policy network        Dimension projection
storage with          decides COMPRESS      (768 → 4096 dims)
zero-copy access      vs EXPAND

Compression Strategies

Strategy	Compression	Use Case
None	1x	Maximum precision
Float16	2x	Good balance
Int8	4x	Memory constrained
Binary	32x	Extreme compression

Policy Networks

Policy	Latency	Description
ThresholdPolicy	~2μs	Cosine similarity threshold
LinearPolicy	~5μs	Single layer classifier
MLPPolicy	~15μs	Two-layer neural network

Quick Start

# Run demo
cargo run --bin refrag-demo

# Run benchmarks
cargo run --bin refrag-benchmark --release

use refrag_pipeline_example::{RefragStore, RefragEntry};

// Create REFRAG-enabled store
let store = RefragStore::new(384, 768)?;

// Insert with representation tensor
let entry = RefragEntry::new("doc_1", search_vector, "The quick brown fox...")
    .with_tensor(tensor_bytes, "llama3-8b");
store.insert(entry)?;

// Hybrid search (policy-based COMPRESS/EXPAND)
let results = store.search_hybrid(&query, 10, Some(0.85))?;

for result in results {
    match result.response_type {
        RefragResponseType::Compress => {
            // Tensor directly injectable into LLM context
            println!("Tensor: {} dims", result.tensor_dims.unwrap());
        }
        RefragResponseType::Expand => {
            // Original text when full context needed
            println!("Text: {}", result.content.unwrap());
        }
    }
}

Target LLM Dimensions

Source	Target	LLM
768	4096	LLaMA-3 8B
768	8192	LLaMA-3 70B
1536	8192	GPT-4

Full Documentation: refrag-pipeline README

🐦 7sense - Bioacoustic Intelligence Platform

What is 7sense?

7sense transforms bird calls into navigable geometric space using cutting-edge AI and vector search. It converts audio recordings of bird songs into rich, searchable embeddings using Perch 2.0 neural networks and ultra-fast HNSW indexing.

Traditional Monitoring	7sense
Expert human listeners	Instant AI species ID
Basic spectrogram analysis	Neural embeddings (1536-dim)
Limited scale	Millions of recordings
Manual pattern finding	Automated discovery

Performance Targets

Metric	Target	Status
HNSW Search Speedup	150x vs brute force	Achieved
Query Latency (p99)	< 50ms	Achieved
Recall@10	>= 0.95	Achieved
Embedding Throughput	> 100 segments/sec	Achieved
Memory per 1M vectors	< 6 GB	Achieved

9 Rust Crates

Crate	Description
sevensense-core	Species taxonomy, temporal types
sevensense-audio	WAV/MP3/FLAC, Mel spectrograms
sevensense-embedding	Perch 2.0 ONNX, 1536-dim vectors
sevensense-vector	HNSW with 150x speedup
sevensense-learning	GNN training, EWC regularization
sevensense-analysis	HDBSCAN clustering, Markov models
sevensense-interpretation	Evidence packs, species narratives
sevensense-api	GraphQL, REST, WebSocket streaming
sevensense-benches	Criterion.rs performance suites

Quick Start

# Build and run
cd examples/vibecast-7sense
cargo build --release
cargo run -p sevensense-api --release

use sevensense_audio::AudioProcessor;
use sevensense_embedding::EmbeddingPipeline;
use sevensense_vector::HnswIndex;

// Load and process audio
let processor = AudioProcessor::new(Default::default());
let segments = processor.process_file("recording.wav").await?;

// Generate Perch 2.0 embeddings
let pipeline = EmbeddingPipeline::new(Default::default()).await?;
let embeddings = pipeline.embed_segments(&segments).await?;

// Search for similar calls (150x faster)
let index = HnswIndex::new(Default::default());
index.add_batch(&embeddings)?;
let neighbors = index.search(&embeddings[0], 10)?;

println!("Found {} similar bird calls", neighbors.len());

Use Cases

• Species Identification - Instant predictions with confidence scores
• Pattern Discovery - Find similar calls across millions of recordings
• Behavioral Insights - Detect singing patterns, dialects, anomalies
• Conservation Monitoring - Track biodiversity at scale

Full Documentation: 7sense README

🧬 EXO-AI - Advanced Cognitive Substrate

What is EXO-AI?

EXO-AI 2025 is a comprehensive cognitive substrate implementing cutting-edge theories from neuroscience, physics, and consciousness research. It provides 9 interconnected Rust crates totaling ~15,800+ lines of research-grade code.

Traditional AI systems process information. EXO-AI aims to understand it.

SIMD-Accelerated Performance

Operation	Speedup	Notes
Distance (128d)	54x	AVX2/NEON optimized
Cosine Similarity	2.73x	Batch operations
Pattern Matching	8-54x	Loop-unrolled
Meta-Simulation	13+ quadrillion/s	From ultra-low-latency-sim

9 Rust Crates

Crate	Description
exo-core	IIT consciousness (Φ) measurement & Landauer thermodynamics
exo-temporal	Temporal memory with causal tracking & consolidation
exo-hypergraph	Topological analysis with persistent homology
exo-manifold	SIREN networks + SIMD-accelerated retrieval
exo-exotic	10 cutting-edge cognitive experiments
exo-federation	Post-quantum federated cognitive mesh
exo-backend-classical	SIMD-accelerated compute backend
exo-wasm	Browser & edge WASM deployment
exo-node	Node.js bindings via NAPI-RS

Key Theories Implemented

Theory	Implementation
IIT (Integrated Information Theory)	Consciousness level (Φ) measurement
Landauer's Principle	Computational thermodynamics
Free Energy Principle	Friston's predictive processing
Strange Loops	Hofstadter's self-referential patterns
Morphogenesis	Pattern formation emergence

Quick Start

[dependencies]
exo-core = "0.1"
exo-temporal = "0.1"
exo-exotic = "0.1"
exo-manifold = "0.1"  # SIMD acceleration!

use exo_core::consciousness::{ConsciousnessSubstrate, IITConfig};
use exo_core::thermodynamics::CognitiveThermometer;

// Measure integrated information (Φ)
let substrate = ConsciousnessSubstrate::new(IITConfig::default());
substrate.add_pattern(pattern);
let phi = substrate.compute_phi();
println!("Consciousness level (Φ): {:.4}", phi);

// Track computational thermodynamics
let thermo = CognitiveThermometer::new(300.0); // Kelvin
let cost = thermo.landauer_cost_bits(1024);
println!("Landauer cost: {:.2e} J", cost);

SIMD Pattern Retrieval

use exo_manifold::{ManifoldEngine, cosine_similarity_simd, batch_distances};

// 54x faster similarity search
let query = vec![0.5; 768];
let results = engine.retrieve(&query, 10)?;

// Batch distance computation
let distances = batch_distances(&query, &database);  // 8-54x speedup

Full Documentation: exo-ai README

---

Database Extensions

🐘 PostgreSQL Extension

The most advanced PostgreSQL vector extension — a drop-in pgvector replacement with 230+ SQL functions, hardware-accelerated SIMD operations, and built-in AI capabilities. Transform your existing PostgreSQL database into a full-featured vector search engine with GNN layers, attention mechanisms, and self-learning capabilities.

# Quick Install from Docker Hub
docker run -d --name ruvector \
  -e POSTGRES_PASSWORD=secret \
  -p 5432:5432 \
  ruvnet/ruvector-postgres:latest

# Connect and use
psql -h localhost -U ruvector -d ruvector_test

# Create extension
CREATE EXTENSION ruvector;

Why RuVector Postgres?

• Zero Migration — Works with existing pgvector code, just swap the extension
• 10x More Functions — 230+ SQL functions vs pgvector's ~20
• 2x Faster — AVX-512/AVX2/NEON SIMD acceleration
• AI-Native — GNN layers, 39 attention mechanisms, local embeddings
• Self-Learning — Improves search quality over time with ReasoningBank

Feature	pgvector	RuVector Postgres
SQL Functions	~20	230+
SIMD Acceleration	Basic	AVX-512/AVX2/NEON (~2x faster)
Index Types	HNSW, IVFFlat	HNSW, IVFFlat + Hyperbolic
Attention Mechanisms	❌	39 types (Flash, Linear, Graph)
GNN Layers	❌	GCN, GraphSAGE, GAT, GIN
Sparse Vectors	❌	BM25, TF-IDF, SPLADE
Self-Learning	❌	ReasoningBank, trajectory learning
Local Embeddings	❌	6 fastembed models built-in
Multi-Tenancy	❌	Built-in namespace isolation
Quantization	❌	Scalar, Product, Binary (4-32x compression)

🐳 Docker Hub (Recommended)

Pull from Docker Hub: hub.docker.com/r/ruvnet/ruvector-postgres

# Quick start
docker run -d --name ruvector \
  -e POSTGRES_PASSWORD=secret \
  -p 5432:5432 \
  ruvnet/ruvector-postgres:latest

# Connect
psql -h localhost -U ruvector -d ruvector_test

# Create extension
CREATE EXTENSION ruvector;

Environment Variables:

Variable	Default	Description
POSTGRES_USER	ruvector	Database user
POSTGRES_PASSWORD	ruvector	Database password
POSTGRES_DB	ruvector_test	Default database

Docker Compose:

version: '3.8'
services:
  ruvector-postgres:
    image: ruvnet/ruvector-postgres:latest
    environment:
      POSTGRES_PASSWORD: secret
      POSTGRES_DB: ruvector_test
    ports:
      - "5432:5432"
    volumes:
      - pgdata:/var/lib/postgresql/data

volumes:
  pgdata:

Available Tags:

• ruvnet/ruvector-postgres:latest - PostgreSQL + RuVector 2.0
• ruvnet/ruvector-postgres:2.0.0 - Specific version

📦 npm CLI

# Install globally
npm install -g @ruvector/postgres-cli

# Or use npx
npx @ruvector/postgres-cli --help

# Commands available as 'ruvector-pg' or 'rvpg'
ruvector-pg --version
rvpg --help

CLI Commands:

# Install extension to existing PostgreSQL
ruvector-pg install

# Create vector table with HNSW index
ruvector-pg vector create table embeddings --dim 1536 --index hnsw

# Import vectors from file
ruvector-pg vector import embeddings data.json

# Search vectors
ruvector-pg vector search embeddings --query "0.1,0.2,..." --limit 10

# Benchmark performance
ruvector-pg bench --iterations 1000

# Check extension status
ruvector-pg status

Programmatic Usage:

import { RuvectorPG } from '@ruvector/postgres-cli';

const client = new RuvectorPG({
  host: 'localhost',
  port: 5432,
  database: 'vectors',
  user: 'postgres',
  password: 'secret'
});

// Create table with HNSW index
await client.createTable('embeddings', {
  dimensions: 1536,
  indexType: 'hnsw',
  distanceMetric: 'cosine'
});

// Insert vectors
await client.insert('embeddings', {
  id: '1',
  vector: [0.1, 0.2, ...],
  metadata: { source: 'openai' }
});

// Search
const results = await client.search('embeddings', queryVector, { limit: 10 });

🦀 Rust Crate

# Install pgrx (PostgreSQL extension framework)
cargo install cargo-pgrx --version "0.12.9" --locked
cargo pgrx init

# Build and install extension
cd crates/ruvector-postgres
cargo pgrx install --release

# Or install specific PostgreSQL version
cargo pgrx install --release --pg-config /usr/lib/postgresql/17/bin/pg_config

Cargo.toml:

[dependencies]
ruvector-postgres = "2.0"

# Optional features
[features]
default = ["pg17"]
pg16 = ["ruvector-postgres/pg16"]
pg15 = ["ruvector-postgres/pg15"]

# AI features (opt-in)
ai-complete = ["ruvector-postgres/ai-complete"]  # All AI features
learning = ["ruvector-postgres/learning"]         # Self-learning
attention = ["ruvector-postgres/attention"]       # 39 attention mechanisms
gnn = ["ruvector-postgres/gnn"]                   # Graph neural networks
hyperbolic = ["ruvector-postgres/hyperbolic"]     # Hyperbolic embeddings
embeddings = ["ruvector-postgres/embeddings"]     # Local embedding generation

Build with all features:

cargo pgrx install --release --features "ai-complete,embeddings"

📝 SQL Examples

-- Enable extension
CREATE EXTENSION ruvector;

-- Create table with vector column
CREATE TABLE documents (
  id SERIAL PRIMARY KEY,
  content TEXT,
  embedding VECTOR(1536)
);

-- Create HNSW index
CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops)
  WITH (m = 16, ef_construction = 200);

-- Insert vectors
INSERT INTO documents (content, embedding)
VALUES ('Hello world', '[0.1, 0.2, ...]'::vector);

-- Semantic search (cosine similarity)
SELECT id, content, embedding <=> '[0.1, 0.2, ...]'::vector AS distance
FROM documents
ORDER BY distance
LIMIT 10;

-- Hybrid search (vector + full-text)
SELECT id, content
FROM documents
WHERE to_tsvector(content) @@ to_tsquery('machine & learning')
ORDER BY embedding <=> query_embedding
LIMIT 10;

-- GNN-enhanced search (with learning)
SELECT * FROM ruvector_gnn_search(
  'documents',
  '[0.1, 0.2, ...]'::vector,
  10,  -- limit
  'gcn' -- gnn_type: gcn, graphsage, gat, gin
);

-- Generate embeddings locally (no API needed)
SELECT ruvector_embed('all-MiniLM-L6-v2', 'Your text here');

-- Flash attention
SELECT ruvector_flash_attention(query, key, value);

See ruvector-postgres README for full SQL API reference (230+ functions).

---

Developer Tools

🛠️ Tools & Utilities

Crate	Description	crates.io
ruvector-bench	Benchmarking suite for vector operations
ruvector-metrics	Observability, metrics, and monitoring
ruvector-filter	Metadata filtering and query predicates
ruvector-collections	Multi-tenant collection management
ruvector-snapshot	Point-in-time snapshots and backups
micro-hnsw-wasm	Lightweight HNSW implementation for WASM

Embedded & IoT

Crate	Description	Target
ruvector-esp32	ESP32/ESP-IDF vector search	ESP32, ESP32-S3
rvlite	SQLite-style edge DB (no_std compatible)	ARM, RISC-V, WASM
micro-hnsw-wasm	<50KB HNSW for constrained devices	WASM, embedded

// ESP32 example (no_std)
#![no_std]
use rvlite::RvLite;

let db = RvLite::new(128);  // 128-dim vectors
db.insert(0, &embedding);
let results = db.search(&query, 5);

---

Browser & Edge (WASM)

🌐 WASM Packages (Browser & Edge)

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

Quick Install (All Browser WASM)

# Core vector search
npm install ruvector-wasm @ruvector/rvlite

# AI & Neural
npm install @ruvector/gnn-wasm @ruvector/attention-wasm @ruvector/sona-wasm

# Graph & Algorithms
npm install @ruvector/graph-wasm @ruvector/mincut-wasm @ruvector/hyperbolic-hnsw-wasm

# Exotic AI
npm install @ruvector/economy-wasm @ruvector/exotic-wasm @ruvector/nervous-system-wasm

# LLM (browser inference)
npm install @ruvector/ruvllm-wasm

Category	Packages	Total Size
Core	ruvector-wasm, rvlite	~200KB
AI/Neural	gnn, attention, sona	~300KB
Graph	graph, mincut, hyperbolic-hnsw	~250KB
Exotic	economy, exotic, nervous-system	~350KB
LLM	ruvllm-wasm	~500KB

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 Systems

🧠 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)

---

Additional Modules

🔬 Scientific OCR (SciPix)

Package	Description	Install
ruvector-scipix	Rust OCR engine for scientific documents	cargo add ruvector-scipix
@ruvector/scipix	TypeScript client for SciPix API	npm install @ruvector/scipix

SciPix extracts text and mathematical equations from images, converting them to LaTeX, MathML, or plain text.

Features:

• Multi-format output — LaTeX, MathML, AsciiMath, plain text, structured JSON
• Batch processing — Process multiple images with parallel execution
• Content detection — Equations, tables, diagrams, mixed content
• Confidence scoring — Per-region confidence levels (high/medium/low)
• PDF support — Extract from multi-page PDFs with page selection

import { SciPixClient, OutputFormat } from '@ruvector/scipix';

const client = new SciPixClient({
  baseUrl: 'http://localhost:8080',
  apiKey: 'your-api-key',
});

// OCR an image file
const result = await client.ocrFile('./equation.png', {
  formats: [OutputFormat.LaTeX, OutputFormat.MathML],
  detectEquations: true,
});

console.log('LaTeX:', result.latex);
console.log('Confidence:', result.confidence);

// Quick LaTeX extraction
const latex = await client.extractLatex('./math.png');

// Batch processing
const batchResult = await client.batchOcr({
  images: [
    { source: 'base64...', id: 'eq1' },
    { source: 'base64...', id: 'eq2' },
  ],
  defaultOptions: { formats: [OutputFormat.LaTeX] },
});

# Rust 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

See npm/packages/scipix/README.md for full documentation.

🔗 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

Native bindings and tools for integrating RuVector into any environment — Node.js, browsers, CLI, or as an HTTP/gRPC server.

Crate	Description	crates.io
ruvector-node	Native Node.js bindings via napi-rs
ruvector-wasm	WASM bindings for browsers & edge
ruvllm-wasm	Browser LLM inference with WebGPU
ruvector-cli	Command-line interface
ruvector-server	HTTP/gRPC server

Node.js (Native Performance)

npm install @ruvector/node

const { RuVector } = require('@ruvector/node');
const db = new RuVector({ dimensions: 1536 });
db.insert('doc1', embedding, { title: 'Hello' });
const results = db.search(queryEmbedding, 10);

Browser (WASM)

npm install @ruvector/wasm

import { RuVectorWasm } from '@ruvector/wasm';
const db = await RuVectorWasm.create({ dimensions: 384 });
await db.insert('doc1', embedding);
const results = await db.search(query, 5);

CLI

cargo install ruvector-cli
ruvector init mydb --dim 1536
ruvector insert mydb --file embeddings.json
ruvector search mydb --query "[0.1, 0.2, ...]" --limit 10

HTTP Server

cargo install ruvector-server
ruvector-server --port 8080 --data ./vectors

# REST API
curl -X POST http://localhost:8080/search \
  -H "Content-Type: application/json" \
  -d '{"vector": [0.1, 0.2, ...], "limit": 10}'

---

Examples & Tutorials

📚 Production Examples

28 production-ready examples demonstrating RuVector integration patterns.

Example	Description	Type
agentic-jujutsu	Quantum-resistant version control for AI agents (23x faster than Git)	Rust
mincut	6 self-organizing network demos: strange loops, time crystals, causal discovery	Rust
subpolynomial-time	n^0.12 subpolynomial algorithm demos	Rust
exo-ai-2025	Cognitive substrate with 9 neural-symbolic crates + 11 research experiments	Rust/TS
neural-trader	AI trading with DRL + sentiment analysis + SONA learning	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 (10-50x speedup)	npm
spiking-network	Biologically-inspired spiking neural networks	Rust
ruvLLM	LLM integration patterns for RAG and AI agents	Rust
onnx-embeddings	Production ONNX embedding generation without Python	Rust
onnx-embeddings-wasm	WASM ONNX embeddings for browsers	WASM
refrag-pipeline	RAG pipeline with vector search and document processing	Rust
scipix	Scientific OCR: equations → LaTeX/MathML with ONNX inference	Rust
graph	Graph database examples with Cypher queries	Rust
edge	364KB WASM edge deployment	Rust
edge-full	Full-featured edge vector DB	Rust
edge-net	Networked edge deployment with zero-cost swarms	Rust
vibecast-7sense	7-sense perception AI application	TypeScript
apify	13 Apify actors: trading, memory engine, synth data, market research	npm
google-cloud	GCP templates for Cloud Run, GKE, Vertex AI	Terraform
wasm-react	React integration with WASM vector operations	WASM
wasm-vanilla	Vanilla JS WASM example for browser vector search	WASM
wasm	Core WASM examples and bindings	WASM
nodejs	Node.js integration examples	Node.js
rust	Core Rust usage examples	Rust

🎓 Tutorials

Tutorial 1: Vector Search in 60 Seconds

import { VectorDB } from 'ruvector';

// Create DB with 384-dimensional vectors
const db = new VectorDB(384);

// Add vectors
db.insert('doc1', [0.1, 0.2, ...]);  // 384 floats
db.insert('doc2', [0.3, 0.1, ...]);

// Search (returns top 5 nearest neighbors)
const results = db.search(queryVector, 5);
// -> [{ id: 'doc1', score: 0.95 }, { id: 'doc2', score: 0.87 }]

Tutorial 2: Graph Queries with Cypher

import { GraphDB } from 'ruvector';

const graph = new GraphDB();

// Create nodes and relationships
graph.query(`
  CREATE (a:Person {name: 'Alice', embedding: $emb1})
  CREATE (b:Person {name: 'Bob', embedding: $emb2})
  CREATE (a)-[:KNOWS {since: 2020}]->(b)
`, { emb1: aliceVector, emb2: bobVector });

// Hybrid query: graph traversal + vector similarity
const results = graph.query(`
  MATCH (p:Person)-[:KNOWS*1..3]->(friend)
  WHERE vector.similarity(friend.embedding, $query) > 0.8
  RETURN friend.name, vector.similarity(friend.embedding, $query) as score
  ORDER BY score DESC
`, { query: queryVector });

Tutorial 3: Self-Learning with SONA

use ruvector_sona::{SonaEngine, SonaConfig};

// Initialize SONA with LoRA adapters
let sona = SonaEngine::with_config(SonaConfig {
    hidden_dim: 256,
    lora_rank: 8,
    ewc_lambda: 0.4,  // Elastic Weight Consolidation
    ..Default::default()
});

// Record successful action
let mut trajectory = sona.begin_trajectory(query_embedding);
trajectory.add_step(result_embedding, vec![], 1.0);  // reward=1.0
sona.end_trajectory(trajectory, true);  // success=true

// SONA learns and improves future predictions
sona.force_learn();

// Later: get improved predictions
let prediction = sona.predict(&new_query_embedding);

Tutorial 4: Dynamic Min-Cut (n^0.12 Updates)

use ruvector_mincut::{DynamicMinCut, Graph};

// Build graph
let mut graph = Graph::new(100);  // 100 nodes
graph.add_edge(0, 1, 10.0);
graph.add_edge(1, 2, 5.0);
graph.add_edge(0, 2, 15.0);

// Compute initial min-cut
let mut mincut = DynamicMinCut::new(&graph);
let (value, cut_edges) = mincut.compute();
println!("Min-cut value: {}", value);  // -> 15.0

// Dynamic update - subpolynomial time O(n^0.12)!
graph.update_edge(1, 2, 20.0);
let (new_value, _) = mincut.recompute();  // Much faster than recomputing from scratch

Tutorial 5: 39 Attention Mechanisms

use ruvector_attention::{
    Attention, FlashAttention, LinearAttention,
    HyperbolicAttention, GraphAttention, MinCutGatedAttention
};

// FlashAttention - O(n) memory, fastest for long sequences
let flash = FlashAttention::new(512, 8);  // dim=512, heads=8
let output = flash.forward(&query, &key, &value);

// LinearAttention - O(n) time complexity
let linear = LinearAttention::new(512, 8);

// HyperbolicAttention - for hierarchical data (Poincaré ball)
let hyper = HyperbolicAttention::new(512, 8, Curvature(-1.0));

// GraphAttention - respects graph structure
let gat = GraphAttention::new(512, 8, &adjacency_matrix);

// MinCutGatedAttention - 50% compute reduction via sparsity
let mincut_gated = MinCutGatedAttention::new(512, 8, sparsity: 0.5);
let sparse_output = mincut_gated.forward(&query, &key, &value);

Tutorial 6: Spiking Neural Networks

import { SpikingNetwork, HDCEncoder } from '@ruvector/spiking-neural';

// High-Dimensional Computing encoder (10K-bit vectors)
const encoder = new HDCEncoder(10000);
const encoded = encoder.encode("hello world");

// Spiking network with BTSP learning
const network = new SpikingNetwork({
  layers: [784, 256, 10],
  learning: 'btsp',  // Behavioral Time-Scale Plasticity
  threshold: 1.0
});

// Train with spike timing
network.train(spikes, labels, { epochs: 10 });

// Inference
const output = network.forward(inputSpikes);

Tutorial 7: Claude Code Hooks Integration

# 1. Initialize hooks
npx @ruvector/cli hooks init

# 2. Install into Claude settings
npx @ruvector/cli hooks install

# 3. Hooks now capture:
#    - File edits (pre/post)
#    - Commands (pre/post)
#    - Sessions (start/end)
#    - Errors and fixes

# 4. Query learned patterns
npx @ruvector/cli hooks recall "authentication error"
# -> Returns similar past solutions

# 5. Get AI routing suggestions
npx @ruvector/cli hooks route "implement caching"
# -> Suggests: rust-developer (confidence: 0.89)

Tutorial 8: Edge Deployment with rvLite

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

// Create persistent edge database (IndexedDB in browser)
const db = await RvLite.create({
  path: 'my-vectors.db',
  dimensions: 384
});

// Works offline - all computation local
await db.insert('doc1', embedding1, { title: 'Hello' });
await db.insert('doc2', embedding2, { title: 'World' });

// Semantic search with metadata filtering
const results = await db.search(queryEmbedding, {
  limit: 10,
  filter: { title: { $contains: 'Hello' } }
});

// Sync when online
await db.sync('https://api.example.com/vectors');

🍕 WASM & Utility Packages

Package	Description	Version	Downloads
@ruvector/wasm	WASM core vector DB
@ruvector/gnn-wasm	WASM GNN layers
@ruvector/graph-wasm	WASM graph DB
@ruvector/attention-wasm	WASM attention
@ruvector/tiny-dancer-wasm	WASM AI routing
@ruvector/router-wasm	WASM semantic router
@ruvector/postgres-cli	Postgres extension CLI
@ruvector/agentic-synth	Synthetic data generator
@ruvector/graph-data-generator	Graph data generation
@ruvector/agentic-integration	Agentic workflows
rvlite	SQLite-style edge DB (SQL/SPARQL/Cypher)

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
• @ruvector/ruvllm-linux-x64-gnu, @ruvector/ruvllm-linux-arm64-gnu, @ruvector/ruvllm-darwin-x64, @ruvector/ruvllm-darwin-arm64, @ruvector/ruvllm-win32-x64-msvc

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 Usage Examples

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 });

🎓 RuvLLM Training & RLM Fine-Tuning Tutorials

Hybrid Routing (90% Accuracy)

RuvLTRA achieves 90% routing accuracy using a keyword-first strategy with embedding fallback:

// Optimal routing: Keywords first, embeddings as tiebreaker
function routeTask(task, taskEmbedding, agentEmbeddings) {
  const keywordScores = getKeywordScores(task);
  const maxKw = Math.max(...Object.values(keywordScores));

  if (maxKw > 0) {
    const candidates = Object.entries(keywordScores)
      .filter(([_, score]) => score === maxKw)
      .map(([agent]) => agent);

    if (candidates.length === 1) return { agent: candidates[0] };
    return pickByEmbedding(candidates, taskEmbedding, agentEmbeddings);
  }

  return embeddingSimilarity(taskEmbedding, agentEmbeddings);
}

Run the benchmark: node npm/packages/ruvllm/scripts/hybrid-model-compare.js

Generate Training Data

# Using CLI (recommended)
npx @ruvector/ruvllm train stats              # View dataset statistics
npx @ruvector/ruvllm train dataset            # Export training data
npx @ruvector/ruvllm train contrastive        # Run full training pipeline

# With options
npx @ruvector/ruvllm train dataset --output ./my-training
npx @ruvector/ruvllm train contrastive --epochs 20 --batch-size 32 --lr 0.0001

Programmatic API:

import { ContrastiveTrainer, generateTrainingDataset, getDatasetStats } from '@ruvector/ruvllm';

const stats = getDatasetStats();
console.log(`${stats.totalExamples} examples, ${stats.agentTypes} agent types`);

const trainer = new ContrastiveTrainer({ epochs: 10, margin: 0.5 });
trainer.addTriplet(anchor, anchorEmb, positive, positiveEmb, negative, negativeEmb, true);
const result = trainer.train();
trainer.exportTrainingData('./output');

Fine-Tune with LoRA

pip install transformers peft datasets accelerate

python -m peft.lora_train \
  --model_name Qwen/Qwen2.5-0.5B-Instruct \
  --dataset ./data/training/routing-examples.jsonl \
  --output_dir ./ruvltra-routing-lora \
  --lora_r 8 --lora_alpha 16 \
  --num_train_epochs 3 \
  --learning_rate 2e-4

Convert to GGUF

# Merge LoRA weights
python -c "
from peft import PeftModel
from transformers import AutoModelForCausalLM
base = AutoModelForCausalLM.from_pretrained('Qwen/Qwen2.5-0.5B-Instruct')
model = PeftModel.from_pretrained(base, './ruvltra-routing-lora')
model.merge_and_unload().save_pretrained('./ruvltra-routing-merged')
"

# Convert and quantize
python llama.cpp/convert_hf_to_gguf.py ./ruvltra-routing-merged --outfile ruvltra-routing-f16.gguf
./llama.cpp/llama-quantize ruvltra-routing-f16.gguf ruvltra-routing-q4_k_m.gguf Q4_K_M

Contrastive Embedding Training

Using RuvLLM CLI (recommended):

# Full contrastive training pipeline with triplet loss
npx @ruvector/ruvllm train contrastive --output ./training-output

# Exports: triplets.jsonl, embeddings.json, lora_config.json, train.sh

Using Python (for GPU training):

from sentence_transformers import SentenceTransformer, losses, InputExample
from torch.utils.data import DataLoader

train_examples = [
    InputExample(texts=["implement login", "build auth component"], label=1.0),
    InputExample(texts=["implement login", "write unit tests"], label=0.0),
]

model = SentenceTransformer("Qwen/Qwen2.5-0.5B-Instruct")
train_loss = losses.CosineSimilarityLoss(model)
model.fit([(DataLoader(train_examples, batch_size=16), train_loss)], epochs=5)

Resources: Issue #122 | LoRA Paper | Sentence Transformers

Rust Training Module

For production-scale dataset generation, use the Rust training module (full docs):

use ruvllm::training::{DatasetGenerator, DatasetConfig};

let config = DatasetConfig {
    examples_per_category: 100,
    enable_augmentation: true,
    seed: 42,
    ..Default::default()
};

let dataset = DatasetGenerator::new(config).generate();
let (train, val, test) = dataset.split(0.7, 0.15, 0.15, 42);
dataset.export_jsonl("training.jsonl")?;

Features:

• 5 agent categories: Coder, Researcher, Security, Architecture, Reviewer (20% each)
• Model routing: Haiku (simple) → Sonnet (moderate) → Opus (complex/security)
• Data augmentation: Paraphrasing, complexity variations, domain transfer
• 8 technical domains: Web, Systems, DataScience, Mobile, DevOps, Security, Database, API
• Quality scores: 0.80-0.96 based on template quality and category
• Performance: ~10,000 examples/second, ~50 MB/s JSONL export

cargo run --example generate_claude_dataset --release
# Outputs: train.jsonl, val.jsonl, test.jsonl, stats.json

---

Project

📁 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.