What performance benchmarks are included in this skill?

The skill includes 2024 performance data for industry-leading systems like Zep, MemGPT, and LangChain, covering recall percentages, latency (P50), and query costs.

When should I use a Knowledge Graph instead of Vector RAG?

Use Knowledge Graphs for complex reasoning and tracking explicit relationships between entities, whereas Vector RAG is better for semantic document search and similarity-based retrieval.

Does this skill include code for memory implementation?

Yes, it provides production-ready Python-based implementation patterns for conversation memory, entity tracking, and tiered memory storage architectures.

How does this help with AI agent persistence?

It provides the architectural blueprints to move memory from short-term context windows to long-term persistent storage, allowing agents to retain user preferences and past learnings across multiple sessions.

AI Memory Systems & Architecture

Name: AI Memory Systems & Architecture
Author: greyhaven-ai

bygreyhaven-ai

•

Data Science & ML

Designs and implements persistent long-term memory systems for AI agents using vector databases, knowledge graphs, and RAG architectures.

About

This skill provides a comprehensive framework for building durable AI memory, bridging the gap between ephemeral context windows and permanent knowledge bases. It equips developers with decision matrices for choosing between Vector RAG, Knowledge Graphs, and hybrid approaches, alongside production-ready implementation patterns for conversation tracking and entity memory. Whether you're optimizing retrieval latency or building complex reasoning agents, this skill offers the benchmarks and architectural guidance needed to ensure AI agents retain and utilize information effectively over time.

Key Features

Standardized implementation patterns for entity memory
16 GitHub stars
Performance benchmarks for Zep, MemGPT, and LangChain
Hybrid retrieval-augmented generation (RAG) frameworks
Multi-tier memory strategies (Hot/Warm/Cold storage)
Vector RAG & Knowledge Graph architecture design

Use Cases

Developing semantic search for large internal documentation sets
Building persistent AI agents that remember user history
Creating structured domain knowledge bases for complex reasoning

About

Key Features

Standardized implementation patterns for entity memory
16 GitHub stars
Performance benchmarks for Zep, MemGPT, and LangChain
Hybrid retrieval-augmented generation (RAG) frameworks
Multi-tier memory strategies (Hot/Warm/Cold storage)
Vector RAG & Knowledge Graph architecture design

Use Cases

Developing semantic search for large internal documentation sets
Building persistent AI agents that remember user history
Creating structured domain knowledge bases for complex reasoning