# NevaMind-AI/memU

Personal memory across agents

Repository: https://github.com/NevaMind-AI/memU
Canonical: https://ross.abutalabs.com/products/memu
Homepage: https://memu.pro
Language: Python
License: NOASSERTION
License Family: other
Topics: mcp, memory, sandbox, agent-memory, claude-skills, skills, openclaw, openclaw-skills, harness, loop-engineering
Last push: 2026-08-26T08:53:06+00:00

## Health v2 (maintenance only)
Score: 77/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 76, longevity 28
- inputs: {"age_days": 401, "days_push": 7, "days_rel": 163, "gap_med": 6, "n_releases_24m": 24}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 14351, forks 1062 (observed 2026-08-28T04:11:06.829265+00:00)

## What it is
memU is a lightweight, agent-driven memory system that gives LLM agents a shared, inspectable personal memory layer across sessions, agents, and devices, stored as readable Markdown wiki files. It automatically distills reusable skills from agent history and supports both a cloud backend and self-hosting with SQLite or Postgres/pgvector.

## Use cases
- give my coding agent persistent memory across sessions
- share context between Claude Code, Cursor, and Codex
- extract reusable skills from my agent chat history
- self-host an agent memory store with embeddings
- remember project preferences across devices
- inject relevant past decisions before new agent tasks

## When to choose
- you use multiple AI coding agents and want one shared memory layer
- you want memory stored as human-readable Markdown you can inspect and edit
- you want automatic skill extraction from agent history
- you want a self-hosted memory backend with SQLite or Postgres

## When to avoid
- you need a general-purpose vector database for application data
- you need a fully supported memory API for a production product rather than agent-driven workflows
- your agent is not among the supported hosts (e.g. Claude chat mode is unsupported)

## Facets
- artifact type: library
- maturity: active
- function: agent-framework, rag, mcp, cli, sdk
- domain: large-language-models, developer-tools
- platform: python, windows, cli, self-hosted, cross-platform
- tags: agent-memory, memory-layer, skill-extraction, claude-skills, markdown-memory, cross-agent, sqlite, pgvector, openclaw, ai-agents, macos, linux

## Member repositories
- NevaMind-AI/memU (main) score 77

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:06.829265+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:12:38.257675+00:00, confidence not recorded.
  - readme: https://github.com/NevaMind-AI/memU (fetched 2026-08-28T04:11:06.829265+00:00, sha fcf8f01695a3)
  - homepage: https://memu.pro (fetched 2026-08-29T08:06:14.757743+00:00, sha 68f4db6271eb)
  - site_page: https://memu.pro/faq (fetched 2026-08-29T08:06:14.767690+00:00, sha f28282a4922a)
- Data as of 2026-08-30T08:39:29.467469+00:00.