How does the agent learn from its mistakes using this skill?

By analyzing episodic memory, the system can extract successful patterns and identify pitfalls, updating its procedural memory to avoid repeating errors in future sessions.

Does the Agent Memory System require an external database?

No, it supports simple file-based JSON storage for local use, but can be scaled to use vector databases like Pinecone or graph databases like Neo4j for advanced needs.

Can I use this for multi-user personalization?

Yes, the semantic and procedural memory layers allow the agent to store and recall specific user preferences and coding styles to provide a tailored experience.

What are the four types of memory this system provides?

The system provides Episodic memory (past events), Procedural memory (learned skills), Semantic memory (knowledge graphs), and Short-term memory (active context).

Agent Memory System

Name: Agent Memory System
Author: adaptationio

byadaptationio

데이터 과학 및 ML

Implements a multi-layered persistent memory architecture that enables AI agents to learn from experience and retain knowledge across sessions.

소개

The Agent Memory System provides a comprehensive framework for building truly adaptive AI agents by implementing four distinct memory types: episodic, procedural, semantic, and short-term. By enabling Claude to store past events, codify learned skills, build structured knowledge graphs, and maintain active context, this skill transforms stateless interactions into a continuous, evolving workflow. It is essential for long-term projects where remembering previous architectural decisions, user preferences, and successful implementation patterns is critical for performance and personalization.

주요 기능

Multi-layered memory architecture covering episodic, procedural, semantic, and short-term types
Knowledge graph support for building structured relationships between facts and concepts
Automated skill extraction that learns and refines patterns from successful task outcomes
Persistent cross-session storage to maintain context over long-running development cycles
Flexible storage options ranging from local JSON files to vector and graph database integrations
0 GitHub stars

사용 사례

Creating highly personalized coding environments that automatically adapt to specific user styles and preferences
Developing self-improving agents that optimize their own implementation patterns based on historical performance
Maintaining deep project context and design rationale throughout multi-week development sprints

소개

주요 기능

Multi-layered memory architecture covering episodic, procedural, semantic, and short-term types
Knowledge graph support for building structured relationships between facts and concepts
Automated skill extraction that learns and refines patterns from successful task outcomes
Persistent cross-session storage to maintain context over long-running development cycles
Flexible storage options ranging from local JSON files to vector and graph database integrations
0 GitHub stars

사용 사례

Creating highly personalized coding environments that automatically adapt to specific user styles and preferences
Developing self-improving agents that optimize their own implementation patterns based on historical performance
Maintaining deep project context and design rationale throughout multi-week development sprints