What is AgentDB Memory Patterns?

It is a specialized skill for Claude Code that provides tools for managing persistent memory, session history, and learning behaviors for AI agents using AgentDB's high-performance storage.

Does it support learning from interactions?

Yes, it includes 9 built-in learning algorithms, such as Decision Transformers and Actor-Critic, to help agents improve their decision-making strategies based on historical data.

How do I integrate this with Claude Code?

You can start the built-in MCP server using the AgentDB CLI and add it to Claude Code with a single command, allowing the assistant to access its own memory database.

How does this skill improve agent performance?

It utilizes HNSW indexing and quantization to deliver vector searches up to 12,500x faster than traditional solutions, ensuring near-instant context retrieval for the agent.

Is it compatible with existing ReasoningBank projects?

Absolutely, it offers 100% backward compatibility and includes migration tools to move legacy ReasoningBank databases into the optimized AgentDB format.

AgentDB Memory Patterns

Name: AgentDB Memory Patterns
Author: Activer007

byActiver007

Data Science & ML

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

About

AgentDB Memory Patterns provides a sophisticated framework for managing AI agent cognition, enabling persistent session memory, long-term fact storage, and pattern-based learning. By leveraging AgentDB's high-performance vector storage and ReasoningBank integration, this skill allows Claude Code and other agents to remember interactions, learn from successes, and maintain deep context across multiple sessions. With performance speeds up to 12,500x faster than traditional RAG solutions, it is an essential tool for developers building intelligent assistants, autonomous agents, and complex chat systems that require an evolving knowledge base and consistent personality.

Key Features

Automatic context synthesis and hierarchical memory consolidation to optimize relevance.
0 GitHub stars
Integrated learning algorithms including Decision Transformers, Q-Learning, and Actor-Critic.
Ultra-fast vector search using HNSW indexing and memory-efficient quantization.
Persistent session and long-term memory management for stateful AI interactions.
Native MCP server support for seamless integration with Claude Code and AI workflows.

Use Cases

Implementing complex RAG systems with high-speed pattern matching and context retrieval.
Building stateful AI assistants that remember user preferences and project-specific history.
Developing autonomous agents that optimize their decision-making through reinforcement learning.

About

Key Features

Automatic context synthesis and hierarchical memory consolidation to optimize relevance.
0 GitHub stars
Integrated learning algorithms including Decision Transformers, Q-Learning, and Actor-Critic.
Ultra-fast vector search using HNSW indexing and memory-efficient quantization.
Persistent session and long-term memory management for stateful AI interactions.
Native MCP server support for seamless integration with Claude Code and AI workflows.

Use Cases

Implementing complex RAG systems with high-speed pattern matching and context retrieval.
Building stateful AI assistants that remember user preferences and project-specific history.
Developing autonomous agents that optimize their decision-making through reinforcement learning.