Frequently Asked Questions About memU
How does memU differ from vector databases or RAG?
Vector DBs store; RAG retrieves. memU *understands*, *prioritizes*, and *evolves*. It adds semantic reasoning, cross-session inference, automatic decay of low-value memories, and hierarchical organization — turning passive storage into active cognition. The result? 92.9% accuracy and 90% lower cost — not incremental improvements, but a paradigm shift.
Can I integrate memU without changing my current stack?
Absolutely. memU is designed as a composable memory layer — drop it in alongside your existing LLM, agent framework, and frontend. No model retraining. No pipeline rewrite. Just add the SDK or connect to the API, configure your memory schema (or use defaults), and let memU handle the rest — including context stitching, deduplication, and relevance ranking.
What deployment options does memU support?
Three production-grade paths — one philosophy: flexibility without compromise. The managed platform offers instant API access and automated scaling. The enterprise edition delivers private deployment, SSO/SAML, custom memory policies, and 24/7 support. The open-source SDK gives full source access, local-first operation, and seamless CI/CD integration — all with identical performance: 92%+ accuracy, ~50ms latency, and plug-and-play simplicity.
Is memU ready for mission-critical applications?
Yes — rigorously. Benchmarked across 12+ real-world agent workloads, memU sustains 92.9% recall accuracy at 10K+ memory entries and delivers sub-50ms retrieval under sustained 500+ RPS load. With uptime SLAs (enterprise), SOC 2 Type II readiness (managed), and MIT-licensed transparency (open-source), memU meets the reliability, security, and scalability demands of customer-facing AI — today.