What is AgentDB and why is it used here?

AgentDB is a high-performance vector database specifically designed for AI agent memory, offering speeds up to 12,500x faster than traditional solutions and full compatibility with ReasoningBank.

Which learning algorithms are supported by this skill?

The skill supports 9 algorithms, including Decision Transformer, Q-Learning, SARSA, Actor-Critic, Curiosity-driven learning, and Federated learning.

How does this skill improve AI agent behavior?

It enables agents to maintain state across sessions, remember specific user facts, and learn from successful interactions using built-in reinforcement learning algorithms.

How does it handle large amounts of memory data?

It uses memory consolidation strategies and quantization (binary or scalar) to reduce memory footprint by 4x to 32x while maintaining high search accuracy.

Is it easy to integrate with Claude Code?

Yes, it includes an MCP (Model Context Protocol) server integration that allows you to add AgentDB capabilities to Claude Code with a single CLI command.

AgentDB Memory Patterns

Name: AgentDB Memory Patterns
Author: ricable

byricable

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Ciencia de Datos y ML

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

Acerca de

This skill provides robust memory management patterns for AI agents, leveraging AgentDB's persistent storage and ReasoningBank integration to enable agents to recall conversations, learn from interactions, and maintain complex context across sessions. It features specialized modules for session history, long-term fact storage, and pattern learning with performance gains up to 12,500x over traditional solutions. By integrating this skill, developers can build stateful, intelligent assistants that improve over time through various reinforcement learning algorithms, hierarchical memory organization, and efficient vector search with HNSW indexing.

Características Principales

Automatic context synthesis and memory consolidation for optimized storage.
2 GitHub stars
HNSW-indexed vector search supporting binary and scalar quantization for 32x memory reduction.
Persistent session and long-term memory management with sub-millisecond retrieval.
Advanced pattern learning using algorithms like Decision Transformer and Actor-Critic.
Seamless MCP server integration for immediate use within Claude Code environments.

Casos de Uso

Developing intelligent agents that learn optimal task-solving strategies through reinforcement learning plugins.
Building stateful AI assistants that remember user preferences and past interactions across multiple sessions.
Managing large-scale vector databases for AI applications requiring high-performance search and retrieval.

Acerca de

Características Principales

Automatic context synthesis and memory consolidation for optimized storage.
2 GitHub stars
HNSW-indexed vector search supporting binary and scalar quantization for 32x memory reduction.
Persistent session and long-term memory management with sub-millisecond retrieval.
Advanced pattern learning using algorithms like Decision Transformer and Actor-Critic.
Seamless MCP server integration for immediate use within Claude Code environments.

Casos de Uso

Developing intelligent agents that learn optimal task-solving strategies through reinforcement learning plugins.
Building stateful AI assistants that remember user preferences and past interactions across multiple sessions.
Managing large-scale vector databases for AI applications requiring high-performance search and retrieval.