Solving Real Agent Memory Failure Modes
Select your agent architecture domain to see how MemHouse eliminates hallucinations, context window overflow, state loss, and tenant data leaks.
Personalized Consumer AI
Consumer AI products live or die on personalization — but personalization at scale has two unsolved problems: context windows overflow and customer data must never bleed between users.
You cannot inject 3 years of conversation history into every prompt. Token budgets explode, latency spikes, and cost becomes unviable at scale.
A shared vector database cannot safely store per-user memory. Without strict account-level isolation, user A's preferences and conversation history leak into user B's context — a critical privacy failure in multi-tenant consumer apps.
The MemHouse Architecture
- Strict Peer Isolation: Every user's conversation history and preferences live in a cryptographically isolated account scope. Zero cross-user data access — even at the database query layer.
- Shared Internal Knowledge Base: Simultaneously exposes a global scope for product knowledge, FAQs, policies, and catalog data shared across all users — no duplication, no contamination.
- Conversation & Preference Memory: Agents remember full conversation history, long-term behavioral patterns, and extracted user preferences (dietary needs, communication style, interests) — without stuffing raw logs into the prompt.
- Continuous Background Ingest: MemHouse quietly extracts durable facts from each conversation ("User prefers evening reminders", "allergic to peanuts") and makes them available for future sessions instantly.
Ready to integrate MemHouse into your agent stack?
Plugs into Python (LangGraph/CrewAI), OpenClaw, Hermes Agent, or any REST/MCP client in minutes.