How do I integrate AgentDB Vector Search with Claude Code?

You can integrate it using the Model Context Protocol (MCP) by running 'claude mcp add agentdb npx agentdb@latest mcp', which exposes semantic search and storage tools to Claude.

Can I use this with third-party embedding models like OpenAI?

Yes, AgentDB is model-agnostic and supports various dimensions including 1536 (OpenAI), 768 (sentence-transformers), and 384 (all-MiniLM-L6-v2).

How fast is AgentDB Vector Search compared to other databases?

AgentDB is engineered for extreme performance, offering search operations 150x to 12,500x faster than traditional vector solutions, typically retrieving results in under 100 microseconds.

What memory optimization techniques does this skill provide?

It supports multiple quantization strategies including Binary (32x reduction), Scalar (4x reduction), and Product Quantization (8-16x reduction) to handle large datasets with minimal RAM.

AgentDB Vector Search

Name: AgentDB Vector Search
Author: ricable

byricable

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数据科学与机器学习

Implements high-performance semantic vector search and intelligent document retrieval for RAG systems and AI agents.

AgentDB Vector Search is a high-performance extension for Claude Code designed to handle large-scale semantic matching and vector-based indexing. By utilizing HNSW indexing and advanced quantization techniques, it provides search speeds up to 12,500x faster than traditional solutions with sub-millisecond latency. This skill is ideal for developers building Retrieval-Augmented Generation (RAG) pipelines, intelligent knowledge bases, and context-aware applications that require efficient similarity lookups and hybrid metadata filtering directly within their AI development workflow.

主要功能

015 GitHub stars

02Advanced quantization (Binary, Scalar, Product) for up to 32x memory reduction.

03Hybrid search capabilities combining vector similarity with complex metadata filtering.

04Built-in MCP server support for seamless integration with Claude Code tools.

05High-performance HNSW indexing for sub-millisecond (<100µs) vector retrieval.

06Integrated RAG support featuring Maximal Marginal Relevance (MMR) for diverse results.

使用场景

01Implementing semantic search engines that require millisecond-level response times.

02Developing long-term memory systems for AI agents using vector-based pattern storage.

03Building high-speed RAG systems for enterprise documentation and internal knowledge bases.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add ricable/ultimate-ai-agent agentdb-vector-search

For use in Claude.ai and ChatGPT

Download Skill