What is QUIC synchronization in AgentDB?

QUIC synchronization enables sub-millisecond, encrypted data synchronization between distributed AgentDB nodes using UDP-based multiplexing for high-speed coordination.

What is MMR and why is it useful?

Maximal Marginal Relevance (MMR) is a technique used to ensure that search results are both relevant to the query and diverse from one another, preventing the AI from receiving redundant information.

How does hybrid search work in this skill?

It allows you to combine vector similarity scores with metadata filtering (e.g., price ranges, categories, or boolean flags) to provide more precise and relevant retrieval.

Does AgentDB support horizontal scaling?

Yes, through database sharding patterns and multi-node synchronization, AgentDB can be scaled horizontally to handle large-scale agentic workloads.

Can I implement custom distance metrics?

While AgentDB provides Cosine, Euclidean, and Dot Product metrics out of the box, the skill provides patterns for implementing custom weighted distance functions for specialized data.

AgentDB Advanced Features

Name: AgentDB Advanced Features
Author: ellisapotheosis

byellisapotheosis

Database Management

Optimizes distributed AI systems with low-latency QUIC synchronization, hybrid vector search, and advanced multi-database management.

About

AgentDB Advanced Features empowers developers to build sophisticated distributed AI applications by providing sub-millisecond cross-node synchronization via the QUIC protocol. It offers fine-grained control over vector search through customizable distance metrics, hybrid search filtering, and Maximal Marginal Relevance (MMR) for result diversification. This skill is essential for production-grade deployments requiring high-performance data consistency, multi-database sharding, and complex context synthesis for agentic reasoning across multiple memories.

Key Features

Sub-millisecond QUIC synchronization for high-speed distributed node coordination.
Maximal Marginal Relevance (MMR) for retrieving diverse, non-redundant search results.
Advanced database management including sharding, connection pooling, and context synthesis.
Support for multiple distance metrics including Cosine Similarity, Euclidean Distance, and Dot Product.
0 GitHub stars
Hybrid search capabilities combining semantic vector similarity with complex metadata filters.

Use Cases

Implementing high-performance RAG pipelines with weighted hybrid search and diverse retrieval.
Building low-latency distributed multi-agent systems that require synchronized memory.
Scaling AI memory horizontally across multiple sharded database instances for production workloads.

About

Key Features

Sub-millisecond QUIC synchronization for high-speed distributed node coordination.
Maximal Marginal Relevance (MMR) for retrieving diverse, non-redundant search results.
Advanced database management including sharding, connection pooling, and context synthesis.
Support for multiple distance metrics including Cosine Similarity, Euclidean Distance, and Dot Product.
0 GitHub stars
Hybrid search capabilities combining semantic vector similarity with complex metadata filters.

Use Cases

Implementing high-performance RAG pipelines with weighted hybrid search and diverse retrieval.
Building low-latency distributed multi-agent systems that require synchronized memory.
Scaling AI memory horizontally across multiple sharded database instances for production workloads.