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ruvector

The fastest vector database for Node.js—built in Rust, runs everywhere

Ruvector is a self-learning vector database with enterprise-grade semantic search, hybrid retrieval (sparse + dense), Graph RAG, FlashAttention-3, and DiskANN — all in a single npm package. Unlike cloud-only solutions or Python-first databases, Ruvector is designed for JavaScript/TypeScript developers who need blazing-fast vector search without external services.

🚀 Sub-millisecond queries • 🎯 52,000+ inserts/sec • 💾 ~50 bytes per vector • 🌍 Runs anywhere • 🧠 859 tests passing

Built by rUv with production-grade Rust performance and intelligent platform detection—automatically uses native bindings when available, falls back to WebAssembly when needed.

🌐 Visit ruv.io | 📦 GitHub | 📚 Documentation

🧠 Claude Code Intelligence v2.0

Self-learning intelligence for Claude Code — RuVector provides optimized hooks with ONNX embeddings, AST analysis, and coverage-aware routing.

Core Features

• 🎯 Smart Agent Routing — Q-learning optimized suggestions with 80%+ accuracy • 📚 9-Phase Pretrain — AST, diff, coverage, neural, and graph analysis • 🤖 Agent Builder — Generates optimized .claude/agents/ configs • 🔗 Co-edit Patterns — Learns file relationships from git history • 💾 Vector Memory — HNSW-indexed semantic recall (150x faster)

New in v2.1 — SOTA Vector Search

• FlashAttention-3 — IO-aware tiled attention, O(N) memory instead of O(N^2) • Graph RAG — Knowledge graph + community detection for multi-hop queries (30-60% improvement) • Hybrid Search — Sparse + dense vectors with RRF fusion (20-49% better retrieval) • DiskANN / Vamana — SSD-friendly ANN graph with PQ compression for large-scale search • ColBERT Multi-Vector — Per-token late interaction retrieval (MaxSim) • Matryoshka Embeddings — Adaptive-dimension search with funnel/cascade modes • MLA — Multi-Head Latent Attention with ~93% KV-cache compression (DeepSeek-V2/V3) • Mamba SSM — Selective State Space Models for linear-time sequence processing • TurboQuant — 2-4 bit KV-cache quantization, 6-8x memory reduction • OPQ — Optimized Product Quantization with learned rotation (10-30% error reduction) • GraphMAE — Graph Masked Autoencoder for self-supervised node learning

New in v2.0

• ONNX WASM Embeddings — all-MiniLM-L6-v2 (384d) runs locally, no API needed • AST Analysis — Symbol extraction, complexity metrics, import graphs • Diff Embeddings — Semantic change classification with risk scoring • Coverage Routing — Test coverage-aware agent selection • Graph Algorithms — MinCut boundaries, Louvain communities, Spectral clustering • 🛡️ Security Scanning — Parallel vulnerability pattern detection • 🎯 RAG Context — Semantic retrieval with HNSW indexing

Performance

| Backend | Read Time | Speedup |

| ONNX inference | ~400ms | baseline | | HNSW search | ~0.045ms | 8,800x | | Memory cache | ~0.01ms | 40,000x |

📖 Full Hooks Documentation →

MCP Server Integration

RuVector includes an MCP server for Claude Code with 97 tools:

Available MCP Tools: • hooks_route, hooks_route_enhanced — Agent routing with signals • hooks_ast_analyze, hooks_ast_complexity — Code structure analysis • hooks_diff_analyze, hooks_diff_classify — Change classification • hooks_coverage_route, hooks_coverage_suggest — Test-aware routing • hooks_graph_mincut, hooks_graph_cluster — Code boundaries • hooks_security_scan — Vulnerability detection • hooks_rag_context — Semantic context retrieval • hooks_attention_info, hooks_gnn_info — Neural capabilities • brain_search, brain_share, brain_status — Shared brain knowledge • decompile_package, decompile_file, decompile_url — Package/file decompilation + witness • edge_status, edge_join, edge_balance — Edge cluster coordination • identity_generate, identity_show — Agent identity

See npx ruvector mcp tools for the full, authoritative tool list.

MCP tool-access policy (default-deny, ADR-256): restrict the exposed/callable tool surface with environment variables — useful for least-privilege deployments.

Precedence is DENY > ALLOW/PROFILE > allow-all. With no policy set, all tools are available (backward compatible). Inspect the active posture with npx ruvector harness status --json (see mcp.accessControl).

MetaHarness, Darwin, and Flywheel

ruvector ships the pinned MetaHarness capability set as direct dependencies. They load lazily, so ordinary vector operations do not pay their startup cost. Darwin candidate execution is not exposed through MCP and requires an explicit --execute flag in the CLI.

The SDK exports capability discovery, routing, promotion and replay verification, Flywheel and Darwin runners, the algorithmic harness kernel, workspace scoring, reward-hack scanning, and credential guards. The read-only MCP profile exposes only non-executing inspection and evaluation tools.

Brain AGI Commands

Access all 8 AGI subsystems deployed at π.ruv.io:

Midstream Commands

Real-time streaming analysis platform:

🌟 Why Ruvector?

The Problem with Existing Vector Databases

Most vector databases force you to choose between three painful trade-offs:

1. Cloud-Only Services (Pinecone, Weaviate Cloud) - Expensive, vendor lock-in, latency issues, API rate limits 2. Python-First Solutions (ChromaDB, Faiss) - Poor Node.js support, require separate Python processes 3. Self-Hosted Complexity (Milvus, Qdrant) - Heavy infrastructure, Docker orchestration, operational overhead

Ruvector eliminates these trade-offs.

The Ruvector Advantage

Ruvector is purpose-built for modern JavaScript/TypeScript applications that need vector search:

🎯 Native Node.js Integration • Drop-in npm package—no Docker, no Python, no external services • Full TypeScript support with complete type definitions • Automatic platform detection with native Rust bindings • Seamless WebAssembly fallback for universal compatibility…

Description from the npm listing.