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Three-Layer Memory Governance: Core, Provisional, Private

#ai #memorygovernance #mrmemory

Here is the article:

title: "Three-Layer Memory Governance: Core, Provisional, Private"
description: "Discover how MrMemory's three-layer memory governance framework ensures secure and efficient management of AI agent memories."
tags: ["AI", "memory governance", "MrMemory"]

date: 2026-04-06

Introduction

In the world of AI agents, memory is a critical component that enables learning, adaptation, and decision-making. However, managing memory effectively can be a daunting task, especially as AI systems become more complex and interconnected. The lack of robust memory governance can lead to issues such as data pollution, incorrect recall, and poor performance. In this article, we'll explore the concept of three-layer memory governance and how MrMemory's API can help you implement it.

What is Three-Layer Memory Governance?

The idea of three-layer memory governance was first proposed by Haichang Li in his paper "Memory as a Service (MaaS): Rethinking Contextual Memory as Service-Oriented Modules for Collaborative Agents". The concept revolves around creating a framework that separates memories into three layers: Core, Provisional, and Private. Each layer serves a specific purpose:

  1. Core: This is the foundation of governance. It's where you store your most valuable and sensitive information.
  2. Provisional: This layer is for temporary or provisional memories that may need to be updated or revised.
  3. Private: This is where you keep personal or private information that requires additional security measures.

By separating memories into these three layers, you can ensure that sensitive data is properly protected and that your AI agents operate efficiently.

How does MrMemory's API Implement Three-Layer Memory Governance?

MrMemory's API provides a simple and efficient way to implement the three-layer memory governance framework. Here are some code examples:

import mrmemory

# Create a client instance
client = MrMemory(api_key="your-key")

# Store a piece of information in the Core layer
client.remember("user prefers dark mode", tags=["preferences"])

# Retrieve information from the Core layer
results = client.recall("what theme does the user like?")
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As you can see, using MrMemory's API is straightforward. You can store and retrieve memories with ease, while also benefiting from the three-layer memory governance framework.

Alternatives: What's Out There?

If you're looking for alternatives to MrMemory's API, there are a few options available:

  1. Mem0: Mem0 provides a similar service-oriented memory architecture, but lacks compression and self-edit tools.
  2. Zep: Zep is a self-hosted solution that requires manual curation and lacks the benefits of MrMemory's three-layer governance framework.
  3. Letta/MemGPT: Letta and MemGPT are also self-hosted solutions that require manual curation and lack the compression and self-edit tools found in MrMemory.

While these alternatives may provide some similar features, they don't offer the same level of efficiency, security, and scalability as MrMemory's API.

Conclusion

In this article, we've explored the concept of three-layer memory governance and how MrMemory's API can help you implement it. By separating memories into Core, Provisional, and Private layers, you can ensure that your AI agents operate efficiently and securely. Try MrMemory today and experience the benefits of a robust memory governance framework.

Try MrMemory

Link to MrMemory documentation

Get Started

Install MrMemory with pip: pip install mrmemory
Install MrMemory with npm: npm install memorymr

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