Local-First Agent Memory

PMB is a local-first, MCP-native memory layer for AI coding agents that stores everything in one SQLite file on your disk and — unusually — measures whether that memory actually changes outcomes. Instead of claiming “+X% faster,” it scores each surfaced lesson against the turn’s real result, so you can see when memory is genuinely helping. Why Coding Agents Forget — and Why “Memory” Is the Hard Part Every coding agent you’ve used — Claude Code, Cursor, Codex, Windsurf, Zed, VS Code, gemini, opencode, continue — starts each session with a clean slate. The model has no persistent recollection of the architecture decisions you made last week, the test suite you just fixed, or the deployment gotcha that cost you an afternoon. This is the “forgetting problem,” and it is the single biggest reason long-running projects feel like they restart from zero every time you open a new session. ...

Introduction — Why Agent Memory Matters in 2026 Agent memory is the single most important infrastructure decision for production AI agents in 2026. Without a persistent memory layer, every agent conversation starts from scratch — no user preferences, no session history, no learned behaviors. The four leading solutions — Déjà Vu, Mem0, Zep, and Agent Memory MCP — take fundamentally different approaches to solving this problem, and choosing the wrong one can cost you in latency, privacy, vendor lock-in, or benchmark credibility. This comparison breaks down each solution’s architecture, performance, pricing, and ideal use case so you can make an informed decision for your agent stack. ...