memU-lite

Lightweight structured memory system for OpenClaw - inspired by memU, zero external dependencies. Provides atomic memory storage with categories (preferences...

Security Scan

The skill's files and runtime instructions match a local, file-based memory manager: it reads/writes a memory tree under ~/.openclaw/workspace and provides shell tools for add/search/backup/clean — nothing in the package requests external credentials or hidden network exfiltration, but there are some packaging/operational caveats to review before installing.

Purpose & Capability

The skill claims to be a lightweight, zero-external-dependency memory system and all scripts act on a local memory directory (~/.openclaw/workspace/memory). No unrelated cloud credentials, network endpoints, or unrelated binaries are required by the skill. The README/SKILL.md references a GitHub repo only as an optional installation source; the runtime behavior is local filesystem operations consistent with the stated purpose. Note: the scripts assume standard Unix utilities (tar, grep, sed, find, stat, etc.), which are not declared as 'required binaries' in the metadata — this is expected for simple shell-based tools but worth knowing.

Instruction Scope

SKILL.md instructs the agent and the user to create/maintain local Markdown memory files and to use the provided Bash tools. The instructions do not tell the agent to read unrelated system files or to send memory to external endpoints automatically. One caveat: PUSH_GUIDE suggests pushing the repo to GitHub and mentions using a Personal Access Token for authentication (user action), which is an operational step, not an automatic exfiltration mechanism. Also, the restore workflow (memu-backup.sh -r) will remove and then extract a tarball into the workspace; restoring an untrusted archive can overwrite files under the workspace (see persistence_privilege notes).

Install Mechanism

There is no formal install spec in the registry (instruction-only), but the package includes an install.sh and helper scripts. No external downloads or obscure URLs are used by the scripts themselves. SKILL.md suggests optionally cloning from a GitHub repo; that is a normal distribution mechanism but means users installing from that remote should trust the repository. Overall install mechanism is low-risk compared to arbitrary remote downloads, but files will be written into the user's home workspace.

Credentials

The skill declares no required environment variables or credentials, and the scripts operate on the local ~/.openclaw/workspace path. No extraneous secrets or unrelated service tokens are requested. The scripts do rely on standard shell utilities being available; they also reference a backup directory under the user's workspace which will store snapshots of memory data (local only).

Persistence & Privilege

always:false (default) and model invocation is allowed by default — appropriate for a user-invoked memory helper. The scripts create and modify files under ~/.openclaw/workspace/memory and backups under ~/.openclaw/workspace/backups — that is expected. Important operational caution: the backup restore command deletes the memory/ directory then extracts the provided tarball into WORKSPACE_DIR. If the archive is untrusted or specially crafted (contains path traversal or absolute paths), extraction could overwrite files under the workspace or beyond it depending on tar behavior; restoring backups should be done only from trusted backups.

Assessment

This package appears to do what it says: local Markdown-based memory management via shell scripts. Before installing, consider: 1) Review scripts yourself (they are plain Bash). They create/modify ~/.openclaw/workspace/memory and backups in ~/.openclaw/workspace/backups — back up any existing memory first. 2) Treat backup archives as trusted: restoring (memu-backup.sh -r) deletes the current memory/ and extracts the archive; do not restore tarballs from untrusted sources because tar extraction can overwrite files. 3) The PUSH_GUIDE suggests pushing to GitHub and mentions using a Personal Access Token — never paste tokens into files or share them publicly. 4) Metadata inconsistencies (packaged _meta.json shows a different ownerId/version than the registry listing) and some minor portability assumptions (sed -i differences on macOS) indicate sloppy packaging but not malicious intent; if you require high assurance, run the scripts in a restricted environment or inspect/normalize them before use. 5) If you intend the agent to call these tools autonomously, be aware they operate on user files and can delete/restore the memory directory; limit autonomous invocation or require user confirmation for destructive operations.

memU-lite - 轻量级结构化记忆系统

灵感来自 memU，零外部依赖，纯 Markdown + OpenClaw 原生工具实现

核心理念

memU-lite 将 Agent 记忆分为三层：

┌─────────────────────────────────────────┐
│  Category Layer: MEMORY.md (概览索引)    │
├─────────────────────────────────────────┤
│  Item Layer: items/{category}/ (原子记忆) │
├─────────────────────────────────────────┤
│  Resource Layer: raw/ (原始记录)         │
└─────────────────────────────────────────┘

安装

方式一：ClawHub (推荐)

openclaw skills install memu-lite

方式二：手动安装

git clone https://github.com/yoo-unison/memu-lite.git
cp -r memu-lite/memory ~/.openclaw/workspace/

快速开始

1. 初始化记忆结构

# 使用提供的初始化脚本
./memu-lite/init.sh

或手动创建：

mkdir -p ~/.openclaw/workspace/memory/{raw,items/{preferences,knowledge,relationships,tasks,skills},indexes}

2. 创建第一条记忆

在 items/preferences/ 下创建文件：

## P-20260302-001 用户决策风格偏好

- **类型**: preference
- **来源**: 2026-03-02 对话
- **日期**: 2026-03-02
- **置信度**: high
- **标签**: #偏好 #决策风格
- **内容**: 
  用户要求独立评估，不盲目跟风，偏好轻量方案。
- **关联**: [[R-20260302-001]]

3. 更新 MEMORY.md 索引

编辑 MEMORY.md 添加记忆条目到索引表格。

记忆类型

类型	目录	前缀	用途
preference	items/preferences/	P	用户偏好、习惯、风格
knowledge	items/knowledge/	K	领域知识、事实信息
relationship	items/relationships/	R	人际关系、组织上下文
task	items/tasks/	T	待办事项、项目
skill	items/skills/	S	技能、方法、流程

记忆 ID 规则

格式：<类型字母>-<日期>-<序号>

示例：

• P-20260302-001 (Preference, 2026-03-02, 第 1 条)
• K-20260302-002 (Knowledge, 2026-03-02, 第 2 条)

记忆文件格式

## [ID] 记忆标题

- **类型**: preference|knowledge|relationship|task|skill
- **来源**: 对话/文档链接
- **日期**: YYYY-MM-DD
- **置信度**: high|medium|low
- **标签**: #标签 1 #标签 2
- **内容**: 
  详细描述...

- **关联**: [[其他记忆 ID]]
- **状态**: active|archived|completed (仅 task 类型需要)

自动化工具（新增）

memU-lite 提供了一套自动化工具脚本，位于 tools/ 目录：

memu-add.sh - 交互式创建记忆

./tools/memu-add.sh

功能：

• 交互式输入记忆内容
• 自动选择记忆类型
• 自动生成 ID
• 自动更新 MEMORY.md 索引

示例：

🧠 memU-lite - 添加新记忆
==========================

选择记忆类型:
  1) preference  - 用户偏好、习惯
  2) knowledge   - 领域知识、事实
  ...

请输入类型编号 (1-5): 1
记忆标题: 用户决策风格偏好
内容摘要: 要求独立评估，不盲目跟风
来源 (如: 对话/文档/链接): 2026-03-02 对话
标签 (用空格分隔，如: #偏好 #AI): #偏好 #决策风格
关联记忆 ID (可选): 

✅ 记忆创建成功!
ID: P-20260302-001
文件: items/preferences/P-20260302-001-用户决策风格偏好.md

memu-search.sh - 搜索记忆

# 按关键词搜索
./tools/memu-search.sh 偏好

# 按标签搜索
./tools/memu-search.sh -t "#AI"

# 按 ID 搜索
./tools/memu-search.sh -i P-20260302-001

# 列出所有记忆
./tools/memu-search.sh -l

memu-backup.sh - 备份与恢复

# 创建备份
./tools/memu-backup.sh

# 列出所有备份
./tools/memu-backup.sh -l

# 恢复备份
./tools/memu-backup.sh -r memory-20260302-143022.tar.gz

# 清理 7 天前的备份
./tools/memu-backup.sh -c 7

memu-tags.sh - 标签索引生成

./tools/memu-tags.sh

功能：

• 自动扫描所有记忆文件的标签
• 生成标签统计报告
• 更新 indexes/tags.md

memu-clean.sh - 过期清理

# 扫描过期记忆
./tools/memu-clean.sh

# 归档过期记忆（推荐）
./tools/memu-clean.sh -a

# 查看已归档记忆
./tools/memu-clean.sh -l

过期机制： 在记忆模板中添加 过期日期 字段：

- **过期日期**: 2026-03-31

到期后会被自动归档到 archive/ 目录。

核心工作流

对话结束后自动记录

1. 提取关键信息（偏好/知识/任务等）
2. 创建原子记忆文件到对应 items/<category>/
3. 更新 MEMORY.md 索引

检索记忆

# 快速概览 - 查看 MEMORY.md
cat ~/.openclaw/workspace/memory/MEMORY.md

# 语义搜索 - 使用 OpenClaw memory_search 工具
memory_search "用户偏好"

# 精确读取 - 使用 OpenClaw memory_get 工具
memory_get --path ~/.openclaw/workspace/memory/items/preferences/P-20260302-001.md

每日收尾

• 汇总当日记忆到 MEMORY.md 时间线
• 更新标签索引 indexes/tags.md

目录结构

memory/
├── MEMORY.md                 # 顶层索引 + 快速概览
├── memu-lite-guide.md        # 使用指南
├── raw/                      # 原始对话记录
│   └── YYYY-MM-DD.md
├── items/                    # 原子化记忆单元
│   ├── TEMPLATE.md           # 记忆模板
│   ├── preferences/          # 用户偏好
│   ├── knowledge/            # 领域知识
│   ├── relationships/        # 人际关系
│   ├── tasks/                # 待办事项
│   └── skills/               # 技能方法
└── indexes/                  # 检索辅助
    ├── tags.md               # 标签索引
    └── timeline.md           # 时间线索引

最佳实践

1. 原子化

每条记忆只包含一个独立事实：

✅ 好：P-20260302-001 决策风格偏好 ❌ 差：P-20260302-001 所有用户信息

2. 及时更新

对话结束后立即记录，避免遗忘关键细节。

3. 标签具体

使用具体标签便于后续检索：

✅ 好：#项目 Alpha #技术栈 #后端 ❌ 差：#知识 #信息

4. 建立关联

相关记忆互相链接：

- **关联**: [[K-20260302-001]], [[S-20260302-001]]

5. 定期维护

• 每周回顾：合并过期任务，更新状态
• 每月清理：归档旧记忆，提炼核心

与原始 memU 对比

特性	memU	memU-lite
自动提取	✅ AI 自动	⚠️ 需 AI 主动记录
主动预判	✅ 后台持续运行	❌ 被动响应
向量检索	✅ 语义搜索	⚠️ 依赖 memory_search
外部依赖	Postgres + API	❌ 无
部署复杂度	中	低
Token 优化	70%+ 压缩	依赖模型上下文

示例记忆

用户偏好 (Preference)

## P-20260302-001 用户决策风格偏好

- **类型**: preference
- **来源**: 2026-03-02 对话
- **日期**: 2026-03-02
- **置信度**: high
- **标签**: #偏好 #决策风格 #AI 使用
- **内容**: 
  1. 要求独立评估 - 不盲目接受外部建议
  2. 借鉴思路而非照搬 - 适配自身情况
  3. 偏好轻量方案 - 无外部依赖
  4. 重视实用性 - 关注实际效果
- **关联**: [[R-20260302-001]]

领域知识 (Knowledge)

## K-20260302-001 编程语言偏好

- **类型**: knowledge
- **来源**: 2026-03-02 对话
- **日期**: 2026-03-02
- **置信度**: high
- **标签**: #技术 #编程语言 #偏好
- **内容**: 
  用户主要使用 Python 和 JavaScript 进行开发。
  偏好简洁、可读性强的代码风格。
- **关联**: [[S-20260302-001]]

技能方法 (Skill)

## S-20260302-001 代码审查清单

- **类型**: skill
- **来源**: 通用最佳实践
- **日期**: 2026-03-02
- **置信度**: high
- **标签**: #技能 #代码审查 #开发流程
- **内容**: 
  Step 1: 检查代码规范和风格
  Step 2: 验证功能逻辑正确性
  Step 3: 确认错误处理完善
  Step 4: 检查性能优化点
  Step 5: 确认测试覆盖关键路径
- **关联**: [[K-20260302-001]]

迁移路径

如果未来需要升级到完整 memU：

1. items/ 目录结构可直接映射到 memU Item Layer
2. MEMORY.md 可作为初始知识库导入
3. 记忆 ID 和标签系统保持一致

脚本工具

init.sh - 初始化记忆结构

#!/bin/bash
mkdir -p ~/.openclaw/workspace/memory/{raw,items/{preferences,knowledge,relationships,tasks,skills},indexes}
echo "memU-lite 记忆结构已创建"

backup.sh - 备份记忆

#!/bin/bash
DATE=$(date +%Y%m%d-%H%M%S)
tar -czf ~/.openclaw/workspace/backups/memory-$DATE.tar.gz ~/.openclaw/workspace/memory/
echo "记忆备份完成：memory-$DATE.tar.gz"

常见问题

Q: 记忆文件太多怎么办？

A: 定期归档旧记忆到 archive/ 目录，保持 items/ 精简。

Q: 如何搜索特定标签的记忆？

A: 使用 grep -r "#标签" ~/.openclaw/workspace/memory/items/

Q: 可以和现有 memory-core 共存吗？

A: 可以。memU-lite 使用相同的 memory_search/memory_get 工具，只是文件结构更规范。

许可证

Apache 2.0

项目地址

GitHub: https://github.com/yoo-unison/memu-lite

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Inspired by memU (https://github.com/NevaMind-AI/memU)