How much faster is AgentDB compared to traditional vector databases?

AgentDB offers operations that are 150x to 12,500x faster than traditional solutions, featuring sub-millisecond search times even at scale.

What is AgentDB Vector Search?

It is a specialized capability for Claude Code that enables high-performance semantic retrieval using vector embeddings, specifically designed for RAG and AI agent workflows.

Can I use this directly within the Claude CLI?

Absolutely. It includes an MCP server integration that allows Claude Code to use tools like agentdb_query and agentdb_store directly in your terminal.

Does this skill support different embedding models?

Yes, it is model-agnostic and supports various dimensions including 1536 (OpenAI), 768 (sentence-transformers), and 384 (MiniLM).

What is quantization in AgentDB?

Quantization is a compression technique that reduces memory usage by 4x to 32x, allowing you to store millions of vectors efficiently without significant loss in search accuracy.

AgentDB Vector Search

Name: AgentDB Vector Search
Author: ricable

byricable

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Gestión de Bases de Datos

Implements high-performance semantic vector search for intelligent document retrieval and context-aware RAG systems.

Acerca de

AgentDB Vector Search is a high-performance database skill designed to integrate lightning-fast semantic retrieval into AI agents and applications. It leverages HNSW indexing and advanced quantization techniques (Binary, Scalar, Product) to achieve search speeds up to 12,500x faster than traditional solutions, making it ideal for large-scale Retrieval-Augmented Generation (RAG) pipelines, similarity matching, and knowledge base management. With both CLI and API support, plus native MCP integration for Claude Code, it allows developers to store, query, and manage millions of vectors with sub-millisecond latency.

Características Principales

High-speed HNSW indexing with sub-millisecond retrieval (<100µs)
Advanced quantization (Binary, Scalar, Product) for up to 32x memory reduction
Native MCP server integration for seamless use within Claude Code
Comprehensive CLI for database initialization, querying, and benchmarking
Hybrid search capabilities combining vector similarity with metadata filtering
2 GitHub stars

Casos de Uso

Developing intelligent knowledge bases with similarity matching
Building efficient Retrieval-Augmented Generation (RAG) systems
Implementing semantic search engines for large documentation sets

Acerca de

Características Principales

High-speed HNSW indexing with sub-millisecond retrieval (<100µs)
Advanced quantization (Binary, Scalar, Product) for up to 32x memory reduction
Native MCP server integration for seamless use within Claude Code
Comprehensive CLI for database initialization, querying, and benchmarking
Hybrid search capabilities combining vector similarity with metadata filtering
2 GitHub stars

Casos de Uso

Developing intelligent knowledge bases with similarity matching
Building efficient Retrieval-Augmented Generation (RAG) systems
Implementing semantic search engines for large documentation sets