What is AC Memory Manager?

It is a Claude Code skill that provides a system for storing and retrieving knowledge across different coding sessions, enabling long-term AI learning.

Is this skill suitable for large projects?

Absolutely, it is ideal for large-scale development where maintaining context across many files and multiple work sessions is critical.

How does it store data?

It integrates with Graphiti and LadybugDB to manage episodic, semantic, and procedural memories in a structured, persistent database.

Why use episodic memory in coding?

Episodic memory tracks past events and experiences, helping the AI avoid repeating mistakes or redundant steps encountered in previous tasks.

Can it remember specific project patterns?

Yes, it is designed to capture architectural decisions, coding styles, and project-specific logic to ensure consistency in future sessions.

AC Memory Manager

Name: AC Memory Manager
Author: adaptationio

byadaptationio

Ciencia de Datos y ML

Persists knowledge and learned patterns across autonomous coding sessions using Graphiti-based memory management.

Acerca de

AC Memory Manager enables Claude to maintain continuity and intelligence across multiple coding sessions by integrating with the Graphiti memory system. It allows the AI to store and retrieve episodic, semantic, and procedural knowledge, effectively building a long-term context for your projects. This skill is essential for complex, multi-day development tasks where remembering architectural decisions, specific bug fixes, and project-specific patterns is crucial for maintaining high productivity and avoiding repetitive errors.

Características Principales

Support for episodic, semantic, and procedural memory types
Persistent cross-session knowledge storage using Graphiti integration
0 GitHub stars
Context-aware retrieval of project-specific coding patterns
Integration with LadybugDB for reliable data persistence
Autonomous learning capabilities that improve AI accuracy over time

Casos de Uso

Tracking past debugging experiences to solve recurring issues more efficiently
Maintaining a persistent knowledge base for autonomous agents working on long-term tasks
Retaining architectural decisions and naming conventions across large-scale projects

Acerca de

Características Principales

Support for episodic, semantic, and procedural memory types
Persistent cross-session knowledge storage using Graphiti integration
0 GitHub stars
Context-aware retrieval of project-specific coding patterns
Integration with LadybugDB for reliable data persistence
Autonomous learning capabilities that improve AI accuracy over time

Casos de Uso

Tracking past debugging experiences to solve recurring issues more efficiently
Maintaining a persistent knowledge base for autonomous agents working on long-term tasks
Retaining architectural decisions and naming conventions across large-scale projects