Does it support data compression?

Yes, it supports scalar and binary quantization, which can reduce memory usage by 4x to 32x while maintaining high retrieval accuracy.

What is AgentDB Memory Patterns?

It's a specialized skill for Claude Code that enables AI agents to maintain persistent memory, learn from interactions, and manage complex context using the AgentDB vector storage engine.

How fast is the search performance?

AgentDB utilizes HNSW indexing, providing vector search results in under 100µs and pattern retrieval in under 1ms when using the built-in cache.

Can I use this with existing ReasoningBank data?

Yes, the skill includes migration tools to move legacy ReasoningBank data to AgentDB with 100% backward compatibility for existing patterns.

What learning algorithms are supported?

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

AgentDB Memory Patterns

Name: AgentDB Memory Patterns
Author: Activer007

byActiver007

数据科学与机器学习

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

关于

This skill provides advanced memory management patterns for building stateful AI agents and intelligent assistants. By leveraging AgentDB's persistent storage and ReasoningBank integration, it enables agents to maintain session context, store long-term facts, and learn from interactions through specialized reinforcement learning algorithms. With performance optimizations that are orders of magnitude faster than traditional solutions, it is ideal for developers creating complex chat systems or autonomous agents that need to evolve and remember user preferences over time.

主要功能

0 GitHub stars
Persistent session and long-term memory storage with vector search
Reinforcement learning plugins including Decision Transformer and Q-Learning
Built-in MCP server support for seamless integration with Claude Code
High-performance HNSW indexing for sub-millisecond pattern retrieval
Memory quantization (binary/scalar) for up to 32x space efficiency

使用场景

Building stateful AI assistants that remember user preferences across multiple sessions
Implementing self-improving agents that learn from successful interaction patterns
Optimizing large-scale vector storage for real-time autonomous agent reasoning

关于

主要功能

0 GitHub stars
Persistent session and long-term memory storage with vector search
Reinforcement learning plugins including Decision Transformer and Q-Learning
Built-in MCP server support for seamless integration with Claude Code
High-performance HNSW indexing for sub-millisecond pattern retrieval
Memory quantization (binary/scalar) for up to 32x space efficiency

使用场景

Building stateful AI assistants that remember user preferences across multiple sessions
Implementing self-improving agents that learn from successful interaction patterns
Optimizing large-scale vector storage for real-time autonomous agent reasoning