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memU

Compiles an agent's raw chat logs, documents, and tool traces into three persistent layers — index, learned skills, and user memory — so context survives sessions. Claims 92% Locomo-benchmark accuracy and up to 95% lower token cost than replaying history.

Introduction

Most agent "memory" is just a vector store bolted onto a chatbot: every message gets dumped in and semantic search hopes to pull the right snippet back. memU treats memory as a compile step instead — raw chat logs, documents, code, and tool traces are restructured into typed, categorized items before retrieval, so the agent reads back curated context rather than a pile of nearest-neighbor chunks.

What Sets It Apart
  • Three layers, not one bucket. It separates a navigable Index (what the agent knows), Skills (reusable patterns and tool usage learned from traces), and Memory (user profile, goals, key events) — each answers a different question instead of cramming everything into one embedding space.
  • A memorize() / retrieve() contract. Ingestion compiles sources into auditable items with source traceability; retrieval serves scoped, ranked context, so you can trace why a fact was recalled rather than trusting an opaque similarity score.
  • Numbers it puts forward: 92% average accuracy on the Locomo benchmark and up to ~95% token-cost reduction versus replaying full history into the prompt.
  • Runs where you need it. Pluggable storage (in-memory, SQLite, Postgres + pgvector) and multi-provider LLM routing (OpenAI, DashScope, Voyage, OpenRouter), with a managed cloud at memu.so if you would rather not self-host.
Great Fit / Look Elsewhere

Great fit if you are building a long-lived coding agent or assistant that must remember project context and user preferences across many sessions, and you value auditable, structured recall over a raw RAG dump. Look elsewhere if you only need short-term context for a single conversation, want a zero-dependency one-file store, or cannot yet adopt its memorize/retrieve workflow — the structure that makes recall reliable also makes it heavier than a plain vector cache.

Where It Fits

It sits alongside Mem0 and MemGPT but leans harder into compiled, multi-layer workspaces rather than a single chronological memory stream — a better match when the agent's value comes from accumulated skills and traceable knowledge, not just recalling past turns.

Information

  • Websitegithub.com
  • OrganizationsNevaMind AI
  • AuthorsNevaMind-AI
  • Published date2025/07/29

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