MemLayer vs Mem0 vs Zep: Choosing the Right Memory System for Your AI Agents

Your AI agent in session 50 should be better than your agent in session 1. But most agents start cold every time — no memory of what worked, what failed, or what the user prefers. The agent memory space has grown fast. Three tools keep showing up in conversations: Mem0, Zep, and MemLayer. They solve the same core problem — persistent context across sessions — but take very different approaches. This post breaks down what each does, how they compare on benchmarks, and when to pick one over the others. The Problem: Agents That Never Learn LLMs are stateless by default. Every session starts from zero. This creates real pain: Users repeat themselves constantly Agents re-discover the same solutions Successful strategies vanish between sessions There is no compounding of knowledge over time Memory systems fix this by giving agents a way to persist and retrieve context. But how they do it matters a lot. Three Approaches to Agent Memory Mem0: Extracted Facts in a Vector Store Mem0 runs an extr