MemHouse vs. Mem0 & Zep: Architectural Differences in Agent Memory Governance

The Fundamental Problem with Raw Agent Memory

When teams start building AI agents, they usually begin by saving raw chat logs into a vector database like Pinecone or standard pgvector. Over time, three critical failure modes emerge:

1. Context Pollution: The agent remembers conflicting or outdated facts (“User lives in NYC” vs “User moved to London”), leading to hallucination loops.
2. Data Leakage Across Tenants: In multi-tenant platforms, Agent A queries context and accidentally retrieves memories belonging to Client B due to weak metadata filtering.
3. Lack of Human Governance: When an agent extracts an incorrect or harmful fact, there is no mechanism to hold or retract that specific piece of knowledge without clearing the entire vector collection.

How MemHouse Solves This Architecture

MemHouse introduces a stateful 5-Step Governance Engine:

• Gate A (Quality & Policy Gate): Every extracted fact is evaluated against confidence scores and custom governance policies. If a fact touches sensitive domain rules or falls below confidence, it enters a Held state for human curator approval.
• Gate B (Blast Radius Check): Cryptographic Account ID verification ensures hard tenant isolation. Facts cannot be written or queried outside authorized Account and Scope scopes.
• Lifecycle State Machine: Facts transition explicitly through Held -> Provisional -> Active -> Retracted, giving CISOs complete control over agent knowledge evolution.