How does this improve AI performance over time?

By analyzing past 'episodes', the agent can identify what worked and what didn't, allowing it to apply proven success patterns and avoid repeating previous debugging errors.

What is the difference between episodic and procedural memory in this skill?

Episodic memory records specific past events (e.g., 'we tried OAuth at 3pm'), while procedural memory extracts general learned skills (e.g., 'the best way to implement OAuth in this repo is X').

Why do I need a separate memory system for AI agents?

Standard LLM context windows are temporary. This system ensures the agent remembers specific decisions, successful patterns, and technical facts across multiple restarts and sessions.

Can this memory be shared across a development team?

Yes. Since the memory is stored in structured files (like JSON) within the repository, it can be committed to version control to act as a shared institutional knowledge base.

Do I need a vector database to use this skill?

No. While it supports vector databases for advanced similarity search, it is fully functional using structured JSON files on your local file system.

Agent Memory System

Name: Agent Memory System
Author: adaptationio

byadaptationio

Data Science & ML

Implement a multi-layered persistent memory architecture for AI agents to enable cross-session learning and long-term context retention.

About

The Agent Memory System provides a sophisticated architectural framework for AI agents to maintain continuity across development sessions. By implementing episodic, procedural, semantic, and short-term memory layers, it allows agents to recall past decisions, codify successful implementation patterns, build knowledge graphs of technical concepts, and manage active working context. This skill is essential for developers building adaptive AI systems that learn from experience and improve performance on long-running projects by preventing the loss of institutional knowledge and project-specific rationale.

Key Features

Knowledge graph support for building relationships between complex technical concepts
Multi-layered memory architecture including episodic, procedural, semantic, and short-term storage
Automated pattern extraction to learn 'skills' from successful past implementations
Persistent context preservation across separate terminal sessions and agent restarts
Flexible storage options ranging from simple JSON files to advanced vector databases
0 GitHub stars

Use Cases

Building self-improving agents that identify and avoid repeating previous implementation failures
Personalizing AI behaviors based on specific user coding styles and repository-specific preferences
Preserving architectural decisions and rationale across multi-week development cycles

About

Key Features

Knowledge graph support for building relationships between complex technical concepts
Multi-layered memory architecture including episodic, procedural, semantic, and short-term storage
Automated pattern extraction to learn 'skills' from successful past implementations
Persistent context preservation across separate terminal sessions and agent restarts
Flexible storage options ranging from simple JSON files to advanced vector databases
0 GitHub stars

Use Cases

Building self-improving agents that identify and avoid repeating previous implementation failures
Personalizing AI behaviors based on specific user coding styles and repository-specific preferences
Preserving architectural decisions and rationale across multi-week development cycles