How does hybrid search work with this skill?

Hybrid search combines vector similarity scores with metadata filters (such as price, category, or date ranges) to provide highly specific and relevant search results.

What is MMR and why should I use it?

Maximal Marginal Relevance (MMR) is a retrieval technique that ensures results are both relevant and diverse, preventing the AI from getting stuck with redundant or repetitive context.

What is QUIC synchronization in AgentDB?

QUIC synchronization allows multiple AgentDB instances to sync data with sub-millisecond latency using encrypted UDP streams, which is ideal for distributed AI agent clusters.

Can I use multiple databases with this skill?

Yes, it provides patterns for multi-database management and sharding, allowing you to separate memory by domain (e.g., code, conversation, and knowledge) for better scaling.

Is this suitable for production deployment?

Absolutely. The skill covers production-grade patterns including connection pooling, error handling for database locks, and CLI tools for database optimization and maintenance.

AgentDB Advanced Features

Name: AgentDB Advanced Features
Author: aegntic

byaegntic

•

Database Management

Optimizes AgentDB implementations with distributed QUIC synchronization, hybrid vector search, and multi-database management patterns.

This skill empowers developers to build high-performance, distributed AI systems by leveraging AgentDB's advanced capabilities. It provides deep expertise in sub-millisecond QUIC synchronization across nodes, complex hybrid search combining vector similarity with metadata filtering, and sophisticated retrieval techniques like Maximal Marginal Relevance (MMR). Ideal for production-grade agentic workflows requiring sharding, custom distance metrics, and efficient context synthesis from multiple memories to ensure non-redundant and highly relevant AI responses.

Key Features

01High-speed QUIC synchronization for <1ms latency between distributed database nodes

02Advanced hybrid search combining vector embeddings with complex metadata filtering

039 GitHub stars

04Flexible distance metrics including Cosine, Euclidean, and Dot Product for specialized data

05Diverse retrieval using Maximal Marginal Relevance (MMR) to prevent redundant AI responses

06Production-ready patterns for database sharding, connection pooling, and context synthesis

Use Cases

01Developing high-precision search engines using hybrid search and diverse result sets

02Scaling AI applications horizontally through database sharding and optimized connection pooling

03Building distributed multi-agent systems that require synchronized memory across global nodes

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add aegntic/cldcde agentdb-advanced

For use in Claude.ai and ChatGPT

Download Skill