Can I migrate from existing ReasoningBank systems?

Yes, the skill includes built-in migration tools for legacy ReasoningBank databases and maintains 100% backward compatibility for seamless transitions.

How does this improve Claude Code performance?

It utilizes HNSW indexing and quantization to retrieve context up to 12,500x faster than traditional methods, ensuring extremely low-latency agent interactions.

What learning algorithms are supported?

The skill supports 9 algorithms including Decision Transformers, Q-Learning, SARSA, and Actor-Critic for advanced sequence modeling and reinforcement learning.

What is AgentDB Memory Patterns?

It is a skill for Claude Code that provides specialized patterns for implementing persistent, long-term memory and pattern learning capabilities in AI agents.

Does it support memory optimization for large databases?

Yes, it includes MemoryOptimizer and ExperienceCurator modules to consolidate similar patterns and prune low-quality data using quantization to reduce size by up to 32x.

AgentDB Memory Patterns

Name: AgentDB Memory Patterns
Author: Microck

byMicrock

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Data Science & ML

Implements high-performance persistent memory and context management for AI agents using AgentDB.

About

AgentDB Memory Patterns provides a comprehensive framework for managing AI agent state through persistent storage and ReasoningBank integration. It enables developers to build intelligent assistants that remember conversations, learn from interactions via reinforcement learning algorithms, and maintain multi-tier context—including immediate, short-term, and long-term memory—across sessions. With features like HNSW indexing and quantization, it offers extreme performance benefits, up to 12,500x faster than traditional solutions, while remaining fully compatible with Claude Code via MCP integration.

Key Features

Multi-tier memory management (Session, Long-Term, and Hierarchical)
Advanced pattern learning with 9 built-in reinforcement learning algorithms
Automated memory consolidation and importance-based pruning
High-performance vector search with HNSW indexing and scalar/binary quantization
Seamless MCP integration for stateful Claude Code workflows
81 GitHub stars

Use Cases

Developing complex intelligent assistants requiring high-speed context retrieval
Building stateful chat systems that remember user preferences across sessions
Creating self-improving agents that learn optimal strategies from past successes

About

Key Features

Multi-tier memory management (Session, Long-Term, and Hierarchical)
Advanced pattern learning with 9 built-in reinforcement learning algorithms
Automated memory consolidation and importance-based pruning
High-performance vector search with HNSW indexing and scalar/binary quantization
Seamless MCP integration for stateful Claude Code workflows
81 GitHub stars

Use Cases

Developing complex intelligent assistants requiring high-speed context retrieval
Building stateful chat systems that remember user preferences across sessions
Creating self-improving agents that learn optimal strategies from past successes