AgentDB Vector Search

About

AgentDB Vector Search is a high-performance database skill designed for Claude Code that enables sub-millisecond semantic retrieval and similarity matching. By leveraging advanced HNSW indexing and quantization techniques, it provides a 150x to 12,500x speed improvement over traditional solutions, making it an ideal choice for building Retrieval-Augmented Generation (RAG) pipelines, semantic search engines, and intelligent knowledge bases. It supports various distance metrics like cosine similarity and Euclidean distance, and integrates seamlessly as an MCP server to provide Claude with direct access to local or remote vector storage.

Key Features

• Built-in MCP server for seamless Claude Code tool integration (query, store, stats)
• 0 GitHub stars
• Sub-millisecond retrieval (<100µs) using advanced HNSW indexing
• Hybrid search capabilities combining vector similarity with metadata filtering
• Quantization options (Binary, Scalar, Product) for up to 32x memory reduction
• Maximal Marginal Relevance (MMR) for diverse and non-redundant search results

Use Cases

• Building local RAG systems for intelligent documentation retrieval
• Managing large-scale vector datasets for AI-driven pattern matching and context-aware querying
• Creating high-performance semantic search engines with low latency requirements