What learning algorithms are available?

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

Is it compatible with existing ReasoningBank projects?

Yes, it is 100% backward compatible with the ReasoningBank API and includes automated migration tools for legacy memory databases.

Does it support Claude Code CLI?

Yes, it includes a built-in MCP (Model Context Protocol) server that allows Claude Code to interact directly with the agent's memory database.

AgentDB is a high-performance vector database and memory layer designed specifically for AI agents, offering significant speed improvements and lower memory overhead than traditional solutions.

How does this skill improve Claude's behavior?

It allows Claude to persist data across sessions, learn from past interactions using reinforcement learning patterns, and retrieve complex context with sub-millisecond latency.

AgentDB Memory Patterns

Name: AgentDB Memory Patterns
Author: ricable

byricable

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Data Science & ML

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

About

This skill equips Claude with advanced memory management capabilities via AgentDB, enabling the creation of stateful agents that remember conversations, learn from interactions, and maintain context across multiple sessions. By leveraging ReasoningBank integration and HNSW indexing, it provides ultra-fast vector search and hierarchical memory consolidation. This is essential for developers building intelligent assistants, complex chat systems, and autonomous agents that require long-term recall, pattern recognition, and optimized context synthesis.

Key Features

Persistent session and long-term memory management
High-performance vector search (150x-12,500x faster than traditional solutions)
Built-in reinforcement learning algorithms including Decision Transformer and Q-Learning
1 GitHub stars
Hierarchical memory organization (Immediate, Short-term, Long-term, Semantic)
Seamless Claude Code integration via Model Context Protocol (MCP) server

Use Cases

Developing autonomous agents that optimize task strategies through pattern learning
Building stateful AI assistants that retain user preferences and history across sessions
Implementing context-rich RAG systems with memory consolidation and pruning

About

Key Features

Persistent session and long-term memory management
High-performance vector search (150x-12,500x faster than traditional solutions)
Built-in reinforcement learning algorithms including Decision Transformer and Q-Learning
1 GitHub stars
Hierarchical memory organization (Immediate, Short-term, Long-term, Semantic)
Seamless Claude Code integration via Model Context Protocol (MCP) server

Use Cases

Developing autonomous agents that optimize task strategies through pattern learning
Building stateful AI assistants that retain user preferences and history across sessions
Implementing context-rich RAG systems with memory consolidation and pruning