Proof-gated graph transformer with 8 verified modules — physics, biological, manifold, temporal, economic graph intelligence via NAPI-RS
WASM bindings for ruvector-graph-transformer: proof-gated graph attention in the browser
Native Node.js bindings for RuVector Graph Database with hypergraph support, Cypher queries, and persistence - 10x faster than WASM
Self-learning vector database for Node.js — hybrid search, Graph RAG, FlashAttention-3, HNSW, 50+ attention mechanisms
DiskANN/Vamana — SSD-friendly billion-scale approximate nearest neighbor search with product quantization
Complete WASM toolkit for edge AI: vector search, graph DB, neural networks, DAG workflows, SQL/SPARQL/Cypher, and ONNX inference - all running in browser
Graph Neural Network capabilities for Ruvector - Node.js bindings
RuVector Format Node.js native bindings
Proper decorator-based transformation / serialization / deserialization of plain javascript objects to class constructors
High-performance vector database with HNSW indexing - 50k+ inserts/sec, built in Rust for AI/ML similarity search and semantic search applications
RuVector Format Node.js native bindings
The Hermes runtime, used by React Native for Android, is able to output [Chrome Trace Events](https://docs.google.com/document/d/1CvAClvFfyA5R-PhYUmn5OOQtYMH4h6I0nSsKchNAySU/preview) in JSON Object Format.
Self-learning LLM runtime — TurboQuant KV-cache (6-8x compression), SONA adaptive learning, FlashAttention, speculative decoding, GGUF inference
RuVector Format Node.js native bindings
WASM bindings for RuvLLM - browser-compatible LLM inference runtime with WebGPU acceleration
Simple dependency graph.
RuVector Format Node.js native bindings
Portable WASM embedding generation with SIMD and parallel workers - run text embeddings in browsers, Cloudflare Workers, Deno, and Node.js
RVF self-learning temporal solver — Thompson Sampling, PolicyKernel, ReasoningBank
RuVector Format WASM microkernel for browser and edge vector operations
Standalone vector database with SQL, SPARQL, and Cypher - powered by RuVector WASM
High-performance WebAssembly attention mechanisms: Multi-Head, Flash, Hyperbolic, MoE, CGT Sheaf Attention with GPU acceleration for transformers and LLMs
TS Compiler transformer for formatjs
Self-learning vector memory for AI agents — single-file .rvf cognitive container with HNSW search, episodic Reflexion memory, causal graph + Cypher, 9 RL algorithms, Thompson Sampling bandit, 41 MCP tools, hybrid (BM25 + dense) retrieval, GNN attention. 1
Node.js bindings for RuVector Graph Transformer via NAPI-RS
Unified graph transformer with proof-gated mutation substrate — 8 verified modules for physics, biological, manifold, temporal, and economic graph intelligence