Does it support Claude Code integration?

Yes, it includes a built-in MCP (Model Context Protocol) server that allows Claude Code to interact directly with the AgentDB memory database.

Can it be used with existing ReasoningBank projects?

Yes, it offers 100% backward compatibility with the ReasoningBank API and includes built-in migration tools for legacy databases.

What learning algorithms are supported?

It supports 9 different algorithms, including Decision Transformers, Q-Learning, Actor-Critic, and SARSA to help agents optimize their behavior over time.

How does it improve performance?

It uses HNSW indexing and caching to achieve search times under 100µs, performing up to 12,500x faster than traditional database solutions for large-scale queries.

What is AgentDB Memory Patterns?

It is a Claude Code skill that provides persistent memory management, enabling AI agents to store sessions, learn from data, and retrieve context efficiently.

AgentDB Memory Patterns

Name: AgentDB Memory Patterns
Author: whrit

bywhrit

Data Science & ML

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

About

AgentDB Memory Patterns provides a robust framework for building stateful AI agents that can remember conversations, learn from interactions, and maintain context across sessions. It integrates AgentDB's persistent storage with ReasoningBank to offer session memory, long-term storage, and pattern learning capabilities. With performance benchmarks up to 12,500x faster than traditional solutions, this skill enables Claude Code to manage complex agent architectures using features like vector-based pattern matching, hierarchical memory organization, and advanced reinforcement learning plugins.

Key Features

High-performance vector storage with HNSW indexing for sub-100µs searches.
Seamless MCP integration to connect AgentDB directly with Claude Code.
Data quantization support for up to 32x reduction in memory footprint.
Advanced reinforcement learning plugins including Decision Transformers and Q-Learning.
0 GitHub stars
Hierarchical memory organization for managing immediate, short-term, and long-term context.

Use Cases

Developing self-improving agents that learn optimal strategies through reinforcement learning patterns.
Building stateful chat systems that remember user preferences and past interactions across sessions.
Managing large-scale knowledge bases for agents requiring fast, semantic context retrieval.

About

Key Features

High-performance vector storage with HNSW indexing for sub-100µs searches.
Seamless MCP integration to connect AgentDB directly with Claude Code.
Data quantization support for up to 32x reduction in memory footprint.
Advanced reinforcement learning plugins including Decision Transformers and Q-Learning.
0 GitHub stars
Hierarchical memory organization for managing immediate, short-term, and long-term context.

Use Cases

Developing self-improving agents that learn optimal strategies through reinforcement learning patterns.
Building stateful chat systems that remember user preferences and past interactions across sessions.
Managing large-scale knowledge bases for agents requiring fast, semantic context retrieval.