Can I use AgentDB Vector Search locally with Claude Code?

Absolutely. It includes a native MCP server integration, allowing Claude Code to query, store, and manage vectors locally or within your distributed infrastructure.

How much faster is AgentDB compared to traditional vector databases?

AgentDB provides 150x to 12,500x faster operations than traditional solutions, with search latencies frequently under 100 microseconds even for large datasets.

Does this skill support different embedding models?

Yes, it is compatible with OpenAI (1536-dim), sentence-transformers (768-dim), all-MiniLM (384-dim), and custom embedding models.

What quantization methods are supported for memory optimization?

It supports Binary (32x memory reduction), Scalar (4x reduction), and Product (8-16x reduction) quantization to balance performance and accuracy.

AgentDB Vector Search

Name: AgentDB Vector Search
Author: EarthmanWeb

byEarthmanWeb

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数据科学与机器学习

Implements ultra-fast semantic vector search and intelligent document retrieval for RAG systems and knowledge bases.

AgentDB Vector Search is a high-performance skill for Claude Code that enables sub-millisecond semantic search using HNSW indexing and advanced quantization techniques. It allows developers to build sophisticated Retrieval-Augmented Generation (RAG) systems by providing 150x to 12,500x faster operations than traditional databases. With native MCP protocol support, it seamlessly integrates with Claude to perform similarity matching, hybrid searches, and context-aware querying directly within the terminal-based development workflow.

主要功能

01Advanced memory efficiency via Binary, Scalar, and Product quantization with up to 32x reduction.

02Native MCP server integration for direct semantic tool access within Claude Code.

032 GitHub stars

04Hybrid search capabilities combining vector similarity with complex metadata filtering.

05Maximal Marginal Relevance (MMR) support to ensure diverse and non-redundant search results.

06Sub-millisecond semantic retrieval using HNSW indexing and O(log n) search complexity.

使用场景

01Building production-grade RAG pipelines with intelligent context retrieval for LLMs.

02Creating semantic search engines for large-scale documentation or internal knowledge bases.

03Implementing real-time similarity matching for recommendation systems or pattern recognition.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add earthmanweb/claude-flow-plugin agentdb-vector-search

For use in Claude.ai and ChatGPT

Download Skill