memU Gives AI Agents a Persistent Memory That Actually Works

Most agents start fresh every session. memU fixes that by building a persistent local knowledge graph that maps your habits, projects, and preferences — then acts on them proactively, without waiting to be asked.

Under the hood, a dedicated memory agent processes conversations and multimodal context into structured memory files. A hybrid retrieval engine (semantic + keyword + contextual) handles recall, while background processing merges duplicates and infers relationships over time. No hand-designed schemas needed.

On the LoCoMo benchmark — the standard test for long-term conversational memory — memU hits 92% accuracy. Token costs drop up to 90% versus typical cloud memory chains because relevant context is retrieved precisely rather than stuffed into every prompt.

Why it matters: persistent, proactive memory is the missing layer between today's stateless agents and genuinely useful AI assistants — and memU ships it as fully open-source.