AgentDB Vector Search

This adds vector search to Claude using AgentDB, which claims 150x to 12,500x faster operations than traditional vector databases with sub-100 microsecond search times. It uses HNSW indexing and quantization under the hood. You'll need Node.js 18+, AgentDB v1.0.7+, and an OpenAI API key for embeddings, though custom models work too. The setup is straightforward with a CLI init command. Worth considering if you're building semantic search features and speed matters, though the performance claims are aggressive enough that you'd want to benchmark against your own data. It's part of the larger ruflo skill collection from ruvnet.

GitHub stars: 67.2K
last updated: Aug 21, 2026

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Install

$ npx -y skills add ruvnet/ruflo --skill 'AgentDB Vector Search' --agent claude-code

Installs into .claude/skills of the current project.

Files

AgentDB Vector Search

What This Skill Does

Implements vector-based semantic search using AgentDB's high-performance vector database with 150x-12,500x faster operations than traditional solutions. Features HNSW indexing, quantization, and sub-millisecond search (<100µs).

Prerequisites

Node.js 18+
AgentDB v1.0.7+ (via agentic-flow or standalone)
OpenAI API key (for embeddings) or custom embedding model

Quick Start with CLI

Initialize Vector Database

# Initialize with default dimensions (1536 for OpenAI ada-002)
npx agentdb@latest init .$vectors.db

# Custom dimensions for different embedding models
npx agentdb@latest init .$vectors.db --dimension 768  # sentence-transformers
npx agentdb@latest init .$vectors.db --dimension 384  # all-MiniLM-L6-v2

# Use preset configurations
npx agentdb@latest init .$vectors.db --preset small   # <10K vectors
npx agentdb@latest init .$vectors.db --preset medium  # 10K-100K vectors
npx agentdb@latest init .$vectors.db --preset large   # >100K vectors

# In-memory database for testing
npx agentdb@latest init .$vectors.db --in-memory