AgentDB Vector Search

About

AgentDB Vector Search provides Claude Code with a high-speed vector database capability, enabling sub-millisecond semantic search and intelligent knowledge retrieval. It features advanced optimizations like HNSW indexing and quantization (Binary, Scalar, Product) to reduce memory usage by up to 32x while maintaining performance that is significantly faster than traditional solutions. This skill is essential for developers building Retrieval-Augmented Generation (RAG) systems, semantic search engines, or context-aware applications that require efficient similarity matching across large datasets directly within their development environment.

Key Features

• Advanced quantization options including Binary and Scalar for up to 32x memory reduction.
• High-performance HNSW indexing for sub-millisecond (<100µs) search retrieval.
• Support for multiple distance metrics including Cosine, Euclidean, and Dot Product.
• Hybrid search capabilities combining vector similarity with complex metadata filtering.
• 0 GitHub stars
• Seamless MCP server integration for direct interaction with Claude Code tools.

Use Cases

• Building local Retrieval-Augmented Generation (RAG) pipelines for private documentation.
• Managing large-scale vector datasets with efficient memory usage on standard hardware.
• Implementing semantic search engines that find relevant code or data based on meaning.