How does AgentDB handle high memory usage for large datasets?

It utilizes multiple quantization strategies like Binary Quantization, which provides a 32x reduction in memory footprint, and Product Quantization for 8-16x reduction.

How fast is AgentDB compared to traditional databases?

AgentDB is engineered for high performance, offering search operations that are 150x to 12,500x faster than traditional solutions, with retrieval times often under 100 microseconds.

Which distance metrics can I use for similarity search?

AgentDB supports Cosine Similarity (best for most text embeddings), Euclidean Distance (L2), and Dot Product metrics.

Can I use AgentDB tools directly within Claude Code?

Absolutely. The skill includes an MCP (Model Context Protocol) server that allows Claude Code to automatically access tools for storing, querying, and analyzing vector data.

Does this skill support different embedding models?

Yes, it is fully compatible with various models including OpenAI (1536-dim), sentence-transformers (768-dim), and MiniLM (384-dim) by adjusting dimensions during initialization.

AgentDB Vector Search

Name: AgentDB Vector Search
Author: ricable

byricable

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Database Management

Implements ultra-fast semantic vector search and HNSW indexing for RAG systems and intelligent document retrieval.

About

AgentDB Vector Search provides a high-performance vector database capability for Claude Code, enabling developers to build sophisticated Retrieval Augmented Generation (RAG) pipelines and semantic search engines. It delivers sub-millisecond search speeds (under 100µs) using advanced HNSW indexing and offers significant memory savings through multiple quantization strategies, including binary and scalar quantization. This skill is ideal for AI-driven projects requiring efficient similarity matching, hybrid search (vector + metadata), and large-scale document processing within an agentic workflow.

Key Features

Sub-millisecond HNSW indexing and retrieval latencies under 100µs
Binary, Scalar, and Product quantization for up to 32x memory reduction
Maximal Marginal Relevance (MMR) to ensure diverse and non-redundant results
1 GitHub stars
Hybrid search combining vector similarity with complex metadata filtering
Native MCP server integration for seamless tool usage within Claude Code

Use Cases

Building high-performance Retrieval Augmented Generation (RAG) systems
Developing context-aware AI agents that require rapid pattern matching and memory
Implementing semantic search engines for large-scale documentation and knowledge bases

About

Key Features

Sub-millisecond HNSW indexing and retrieval latencies under 100µs
Binary, Scalar, and Product quantization for up to 32x memory reduction
Maximal Marginal Relevance (MMR) to ensure diverse and non-redundant results
1 GitHub stars
Hybrid search combining vector similarity with complex metadata filtering
Native MCP server integration for seamless tool usage within Claude Code

Use Cases

Building high-performance Retrieval Augmented Generation (RAG) systems
Developing context-aware AI agents that require rapid pattern matching and memory
Implementing semantic search engines for large-scale documentation and knowledge bases