Does this support reinforcement learning for agents?

Yes, it includes 9 built-in learning algorithms, including Decision Transformers and Actor-Critic models, to help agents improve their strategies based on successful interactions.

Is it compatible with the Model Context Protocol (MCP)?

Yes, it provides a built-in MCP server that can be added to Claude Code with a single command, allowing Claude to interact directly with the agent's memory database.

How much faster is AgentDB compared to traditional solutions?

AgentDB offers significant performance gains, ranging from 150x faster pattern search to 12,500x faster large-scale queries due to its HNSW indexing and caching.

What is AgentDB Memory Patterns?

It is a specialized skill for Claude Code that enables persistent memory and pattern learning for AI agents using the AgentDB vector database and ReasoningBank integration.

Can I use this for long-term user preference storage?

Yes, the skill includes specific patterns for storing and retrieving facts, such as user preferences, with high confidence scores and source tracking.

AgentDB Memory Patterns

Name: AgentDB Memory Patterns
Author: Activer007

byActiver007

Data Science & ML

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

About

AgentDB Memory Patterns provides a robust framework for managing AI agent state across sessions, enabling long-term memory, hierarchical organization, and context-aware reasoning. By leveraging AgentDB’s high-performance vector storage and ReasoningBank integration, it allows agents to learn from past interactions through reinforcement learning algorithms and retrieve context up to 12,500x faster than traditional methods. This skill is an essential toolkit for developers building complex chat systems, intelligent assistants, or autonomous agents that need to maintain continuity and improve performance over time through experience.

Key Features

Persistent session and long-term memory storage using high-performance vector databases.
MCP server integration for seamless memory management within the Claude Code environment.
0 GitHub stars
Advanced pattern learning with 9 reinforcement learning algorithms including Decision Transformer and Q-Learning.
Efficient data management with quantization techniques for up to 32x memory footprint reduction.
Hierarchical memory organization across immediate, short-term, long-term, and semantic layers.

Use Cases

Implementing high-speed context retrieval for large-scale RAG applications requiring sub-millisecond latency.
Building stateful AI assistants that remember user preferences and conversation history across multiple sessions.
Developing autonomous agents that optimize decision-making through pattern matching and reinforcement learning.

About

Key Features

Persistent session and long-term memory storage using high-performance vector databases.
MCP server integration for seamless memory management within the Claude Code environment.
0 GitHub stars
Advanced pattern learning with 9 reinforcement learning algorithms including Decision Transformer and Q-Learning.
Efficient data management with quantization techniques for up to 32x memory footprint reduction.
Hierarchical memory organization across immediate, short-term, long-term, and semantic layers.

Use Cases

Implementing high-speed context retrieval for large-scale RAG applications requiring sub-millisecond latency.
Building stateful AI assistants that remember user preferences and conversation history across multiple sessions.
Developing autonomous agents that optimize decision-making through pattern matching and reinforcement learning.