Can I integrate this with Claude Code directly?

Yes, it includes a built-in MCP (Model Context Protocol) server that allows you to add AgentDB capabilities to Claude Code with a single command.

What is the primary benefit of using AgentDB Memory Patterns?

It provides AI agents with persistent, high-speed memory and learning capabilities, allowing them to remember past interactions and improve their performance through reinforcement learning algorithms.

How does it improve search performance compared to traditional solutions?

By utilizing HNSW indexing and optimized vector search, it can achieve retrieval speeds up to 12,500x faster than legacy systems, often delivering results in under 100 microseconds.

What kind of learning algorithms are supported?

The skill supports 9 algorithms, including Decision Transformers, Q-Learning, SARSA, Actor-Critic, and Curiosity-Driven exploration, all accessible via learning plugins.

AgentDB Memory Patterns

Name: AgentDB Memory Patterns
Author: ricable

byricable

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Data Science & ML

Implements high-performance persistent memory and reinforcement learning patterns for AI agents using AgentDB and ReasoningBank.

About

AgentDB Memory Patterns is a specialized toolset for Claude Code that enables the creation of stateful AI agents through AgentDB's persistent storage and ReasoningBank integration. It provides sophisticated memory management patterns, including session-based, long-term, and hierarchical memory, while utilizing advanced learning algorithms like Decision Transformers and Q-Learning. By integrating vector search and HNSW indexing, it delivers massive performance gains—up to 12,500x faster than traditional solutions—making it an essential framework for building intelligent assistants that remember interactions, maintain context across sessions, and learn from user feedback.

Key Features

Persistent session and long-term memory storage using AgentDB
High-performance vector search with HNSW indexing and quantization support
9 integrated learning algorithms including Decision Transformers and Actor-Critic
MCP server compatibility for seamless real-time integration with Claude Code
Automated memory consolidation and hierarchical context management
2 GitHub stars

Use Cases

Managing large-scale vector knowledge bases with low-latency retrieval and high memory efficiency
Building stateful AI assistants that maintain user context and preferences across multiple sessions
Implementing reinforcement learning patterns to improve agent decision-making based on past outcomes

About

Key Features

Persistent session and long-term memory storage using AgentDB
High-performance vector search with HNSW indexing and quantization support
9 integrated learning algorithms including Decision Transformers and Actor-Critic
MCP server compatibility for seamless real-time integration with Claude Code
Automated memory consolidation and hierarchical context management
2 GitHub stars

Use Cases

Managing large-scale vector knowledge bases with low-latency retrieval and high memory efficiency
Building stateful AI assistants that maintain user context and preferences across multiple sessions
Implementing reinforcement learning patterns to improve agent decision-making based on past outcomes