What is AgentDB Vector Search?

It is a high-performance vector database skill for Claude Code that enables semantic search, similarity matching, and intelligent document retrieval for AI applications.

How fast is AgentDB compared to traditional solutions?

AgentDB is benchmarked at 150x to 12,500x faster than traditional solutions, offering sub-millisecond search times (<100µs) through optimized HNSW indexing.

What embedding models are compatible with this skill?

It is model-agnostic and supports any embedding model, including OpenAI's ada-002, sentence-transformers, and all-MiniLM-L6-v2, with customizable dimensions.

Does it support Retrieval-Augmented Generation (RAG)?

Yes, it is specifically designed to facilitate RAG pipelines by efficiently storing embeddings and retrieving relevant context to ground LLM responses.

How does AgentDB handle high memory usage?

It features multiple quantization strategies (Binary, Scalar, and Product) that can reduce the memory footprint of high-dimensional vectors by 4x to 32x.

AgentDB Vector Search

Name: AgentDB Vector Search
Author: aegntic

byaegntic

•

Data Science & ML

Enables high-performance semantic vector search and intelligent document retrieval for RAG systems and knowledge bases.

AgentDB Vector Search provides an ultra-fast, local-first vector database solution designed for building robust Retrieval-Augmented Generation (RAG) pipelines and semantic search engines within Claude Code. By leveraging HNSW indexing and advanced quantization techniques, it delivers sub-millisecond search results—up to 12,500x faster than traditional solutions—while significantly reducing memory overhead. It is an essential skill for developers needing context-aware querying, similarity matching, and efficient storage of high-dimensional embeddings directly in their AI-powered development workflow.

Key Features

019 GitHub stars

02Sub-millisecond semantic search using HNSW indexing with O(log n) complexity

03Full MCP server integration for seamless use with Claude Code internal tools

04Hybrid search capabilities combining vector similarity with metadata filtering

05Advanced quantization (Binary, Scalar, Product) for up to 32x memory reduction

06Support for multiple distance metrics including Cosine, Euclidean, and Dot Product

Use Cases

01Building local RAG (Retrieval-Augmented Generation) systems for documentation or private codebases

02Implementing intelligent semantic search engines capable of handling millions of vectors

03Creating context-aware knowledge bases that provide relevant data slices to AI models

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add aegntic/cldcde agentdb-vector-search

For use in Claude.ai and ChatGPT

Download Skill