Does this skill support Claude Code directly?

Yes, it includes a built-in MCP (Model Context Protocol) server that allows Claude Code to interact directly with your AgentDB instance for seamless memory management.

How fast is AgentDB compared to other solutions?

AgentDB is engineered for extreme performance, offering search and retrieval speeds between 150x and 12,500x faster than traditional database implementations.

What learning algorithms are included?

The skill supports 9 algorithms, including Decision Transformer, Q-Learning, SARSA, Actor-Critic, and Curiosity-Driven learning for advanced agent behavior development.

Can I migrate existing data to AgentDB?

Yes, it provides automated migration tools for legacy systems like ReasoningBank and maintains 100% backward compatibility with the ReasoningBank API.

What is AgentDB Memory Patterns?

It is a specialized skill for Claude Code that enables AI agents to store and retrieve persistent memories, learn from patterns, and maintain state across multiple sessions.

AgentDB Memory Patterns

Name: AgentDB Memory Patterns
Author: ricable

byricable

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データサイエンスとML

Implements high-performance persistent memory and pattern learning for stateful AI agents using AgentDB.

概要

AgentDB Memory Patterns provides a sophisticated framework for managing AI agent state, enabling long-term memory, session persistence, and behavioral pattern learning. By leveraging AgentDB's high-speed vector storage and ReasoningBank integration, it allows Claude and other agents to maintain context across sessions with performance speeds up to 12,500x faster than traditional methods. This skill is essential for developers building intelligent assistants, complex chat systems, or autonomous agents that need to evolve and remember user interactions over time through structured memory consolidation and hierarchical storage.

主な機能

9 advanced learning algorithms including Decision Transformers and Q-Learning
2 GitHub stars
High-speed vector search with <100µs latency using HNSW indexing
Persistent session and long-term memory storage
Seamless MCP server integration for Claude Code workflows
Memory quantization (scalar/binary) for up to 32x space efficiency

ユースケース

Implementing autonomous agents capable of learning from successful task completions
Creating high-performance chat systems with deep context retrieval and synthesis
Building stateful AI assistants that remember user preferences and past interactions

概要

主な機能

9 advanced learning algorithms including Decision Transformers and Q-Learning
2 GitHub stars
High-speed vector search with <100µs latency using HNSW indexing
Persistent session and long-term memory storage
Seamless MCP server integration for Claude Code workflows
Memory quantization (scalar/binary) for up to 32x space efficiency

ユースケース

Implementing autonomous agents capable of learning from successful task completions
Creating high-performance chat systems with deep context retrieval and synthesis
Building stateful AI assistants that remember user preferences and past interactions