Can I migrate existing data from ReasoningBank?

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

How fast is the vector search in AgentDB?

AgentDB provides ultra-fast performance, ranging from 150x to 12,500x faster than traditional solutions, with search times often under 100 microseconds.

Does it support reinforcement learning?

Yes, it includes 9 different learning algorithms, including Decision Transformer, Q-Learning, and Actor-Critic, to help agents improve through experience.

How does it handle large memory growth?

AgentDB supports scalar and binary quantization, which can reduce memory footprint by 4x to 32x while maintaining high search accuracy.

What is AgentDB Memory Patterns?

It is a skill for Claude Code that enables AI agents to store, retrieve, and learn from interaction data using a high-performance persistent memory system.

AgentDB Memory Patterns

Name: AgentDB Memory Patterns
Author: zellycloud

byzellycloud

Data Science & ML

Manages persistent AI agent memory and reasoning patterns using high-performance vector storage and learning algorithms.

About

AgentDB Memory Patterns provides a sophisticated framework for building stateful AI agents within Claude Code. By leveraging AgentDB's ultra-fast vector search and ReasoningBank integration, it enables agents to maintain session history, store long-term facts, and learn from past interactions through reinforcement learning plugins. This skill is essential for developers building complex assistants, autonomous agents, or chat systems that require context-aware reasoning and high-speed retrieval of historical data, offering performance up to 12,500x faster than traditional solutions.

Key Features

Automatic context synthesis and memory consolidation
100% backward compatibility with ReasoningBank API
Persistent session and long-term memory management
0 GitHub stars
High-performance vector search with HNSW indexing and quantization
Integrated reinforcement learning plugins like Q-Learning and Actor-Critic

Use Cases

Implementing pattern-matching for repetitive task automation in agent workflows
Creating autonomous agents that improve performance over time via reinforcement learning
Building stateful AI assistants that remember user preferences across sessions

About

Key Features

Automatic context synthesis and memory consolidation
100% backward compatibility with ReasoningBank API
Persistent session and long-term memory management
0 GitHub stars
High-performance vector search with HNSW indexing and quantization
Integrated reinforcement learning plugins like Q-Learning and Actor-Critic

Use Cases

Implementing pattern-matching for repetitive task automation in agent workflows
Creating autonomous agents that improve performance over time via reinforcement learning
Building stateful AI assistants that remember user preferences across sessions