What is AgentDB Memory Patterns?

It is a specialized skill for Claude Code that provides memory management patterns for AI agents, allowing them to store, retrieve, and learn from interactions using AgentDB's persistent vector storage.

How much faster is AgentDB compared to traditional solutions?

AgentDB is designed for extreme performance, offering search speeds 150x to 12,500x faster than traditional database solutions, with vector search latency typically under 100 microseconds.

Which learning algorithms are included?

The skill supports 9 distinct algorithms for agent improvement, including Decision Transformers, Q-Learning, SARSA, Actor-Critic, and Curiosity-driven exploration.

Does this skill work with Claude Code's MCP?

Yes, it includes a dedicated MCP (Model Context Protocol) server that can be added to Claude Code with a single command, enabling the AI to interact with your agent's memory database.

What are the benefits of quantization in AgentDB?

Quantization reduces the memory footprint of your vector database by 4x (scalar) to 32x (binary), allowing you to store millions of patterns with minimal RAM usage while maintaining high accuracy.

AgentDB Memory Patterns

Name: AgentDB Memory Patterns
Author: ricable

byricable

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Ciencia de Datos y ML

Manages persistent memory and pattern learning for AI agents using high-performance vector storage and context synthesis.

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AgentDB Memory Patterns provides a specialized framework for building stateful AI agents that can remember conversations, learn from past interactions, and maintain long-term context. By leveraging AgentDB's high-performance vector search—which performs up to 12,500x faster than traditional solutions—this skill enables developers to implement complex memory hierarchies including session, short-term, and long-term storage. It features integrated reinforcement learning algorithms and ReasoningBank support, making it an essential tool for creating intelligent assistants that require continuity, adaptive behavior, and sub-millisecond context retrieval.

Características Principales

Multi-tier memory management including session, long-term, and hierarchical patterns
9 built-in learning algorithms including Decision Transformers and Actor-Critic models
High-performance vector search using HNSW indexing with <100µs latency
Seamless MCP integration for direct use within Claude Code environments
Efficient data quantization (binary/scalar) for up to 32x memory footprint reduction
2 GitHub stars

Casos de Uso

Building stateful AI chat systems that retain context across multiple user sessions
Implementing high-speed semantic retrieval for large-scale Retrieval-Augmented Generation (RAG)
Developing adaptive agents that improve their decision-making through reinforcement learning

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Características Principales

Multi-tier memory management including session, long-term, and hierarchical patterns
9 built-in learning algorithms including Decision Transformers and Actor-Critic models
High-performance vector search using HNSW indexing with <100µs latency
Seamless MCP integration for direct use within Claude Code environments
Efficient data quantization (binary/scalar) for up to 32x memory footprint reduction
2 GitHub stars

Casos de Uso

Building stateful AI chat systems that retain context across multiple user sessions
Implementing high-speed semantic retrieval for large-scale Retrieval-Augmented Generation (RAG)
Developing adaptive agents that improve their decision-making through reinforcement learning