memory_plus
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@memory_plusstore that my api key is sk-abc123"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
🧠 Memory Plus
一个为 OpenClaw 和 Hermes Agent 设计的记忆管理组件,基于 SVM(Structured Visual Memory)架构,集成 Zettelkasten 知识笔记双向同步,让 AI Agent 拥有持久化、可检索的结构化记忆。
English · 简体中文
✨ 核心功能
功能 | 描述 |
🧠 内存存储 | 内存 LRU 缓存 + SQLite 持久化,支持多租户隔离 |
🔍 关键词匹配 | Aho-Corasick 多模式匹配引擎(pyahocorasick / 纯 Python 回退) |
📋 审计日志 | SQLite 存储的 store/recall/forget/config 操作事件日志 |
🔄 Zettelkasten 同步 | 双向同步:SVM→ZK(冷数据备份)+ ZK→SVM(热加载重要/近期/常青笔记) |
🛡️ 淘汰保护 | LRU 淘汰前自动同步到 ZK,防止数据丢失 |
⚖️ 准入控制 | 可配置最低权重和压力阈值,保护高价值记忆 |
📥 导入迁移 |
|
🔌 MCP Server | 内置 stdio 协议的 MCP 服务,接入 Hermes / OpenClaw 等 Agent 框架 |
🐳 Docker 支持 | 4 种容器环境预装 SVM(Hermes + 3 版本 OpenClaw) |
🎯 命令行 | 完整的 CLI 界面,支持 JSON 输出,跨语言调用 |
Related MCP server: engram
⚡ 性能基准
测试环境: Python 3.12.3, SQLite WAL
测试规模: 553K blocks/sec 存储 · 52K matches/sec Aho-Corasick(5000 关键词)
当前测试套件: 80 个单元测试全部通过 ✅
🇺🇸 Looking for English documentation? Click here for English
🚀 快速开始
AI Agent 一句话安装
curl -fsSL https://raw.githubusercontent.com/cx2002302-lang/memory_plus/master/scripts/quick-install.sh | bashpip 安装
pip install memory-plus或从源码安装(推荐开发模式):
git clone https://github.com/cx2002302-lang/memory_plus.git
cd memory_plus
pip install -e ".[test]"
# 运行测试
pytest tests/CLI 基本用法
# 存储记忆
svm store --key my_key --value "记忆内容"
# 检索记忆
svm recall --keyword kw1 --keyword kw2
# 与 Zettelkasten 同步
svm sync auto
# 导入旧版记忆
svm import --source ~/.openclaw/memory/main.sqlite
# 搜索(SVM + ZK)
svm search "关键词"
# 查看状态
svm statsDocker 部署
# 使用 svm-deploy skill(需要先安装 skill)
svm-deploy
# 或手动挂载:
# svm 数据库路径: ~/.openclaw/svm/memory.db
# ZK 数据库路径: ~/.openclaw/zettelkasten/zettelkasten.db🧩 MCP 工具(用于 AI Agent)
工具 | 权限 | 描述 |
| 写入 | 存储一个记忆块 |
| 读取 | 按关键词检索记忆块 |
| 写入 | 删除指定记忆块 |
| 读取 | 列出所有记忆块 |
| 读取 | 获取内存统计信息 |
| 读取 | 查询审计日志 |
🛡️ 数据安全
Memory Plus 与 Zettelkasten 双向同步遵循以下安全原则:
操作 | 安全策略 |
SVM → ZK 写入 | 仅 INSERT,永不 UPDATE/DELETE/DROP |
ZK → SVM 读取 | 只读 QUERY,不修改 ZK 数据 |
标签写入 |
|
淘汰保护 | LRU 淘汰前先同步到 ZK,防止数据丢失 |
准入控制 | 内存使用率 ≥ 80% 时拒绝低权重(< 0.1)写操作 |
FTS5 搜索 | 使用 |
⚠ 重要警告:切勿在已有数据的 ZK 数据库上运行
openclaw zk init。migrateNotesTableForArchive()可能重新创建zettel_notes表并导致数据丢失。 详见 Schema 兼容性文档。
📁 项目结构
memory_plus/
├── svm/ # Python 模块
│ ├── __init__.py # 版本号
│ ├── cli.py # CLI 入口
│ ├── config.py # 配置管理(预设、自动检测内存)
│ ├── audit.py # 审计日志
│ ├── exceptions.py # 异常体系
│ ├── injector.py # 上下文注入器
│ ├── mcp_server.py # MCP 服务
│ ├── models/ # 数据模型
│ │ └── block.py # MemoryBlock(核心内存块)
│ ├── store/ # 存储层
│ │ ├── memory_store.py # 内存 LRU 缓存
│ │ └── persistent.py # SQLite 持久化
│ ├── sync/ # Zettelkasten 同步引擎
│ │ ├── engine.py # 同步编排
│ │ └── zk_sync.py # ZK 数据库读写
│ └── trigger/ # 检索触发
│ ├── matcher.py # Aho-Corasick 关键词匹配
│ └── strategy.py # 检索策略
├── tests/ # 测试套件
│ ├── test_basic.py # 58 个基础测试
│ ├── test_import.py # 8 个导入测试
│ ├── test_sync.py # 18 个同步测试
│ └── test_perf.py # 性能基准测试
├── image/ # 配图
├── docs/ # 文档
├── CHANGELOG.md
├── LICENSE
└── README.md📜 许可证
MIT © Memory Plus Contributors
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Servers
- Alicense-qualityCmaintenanceProvides persistent memory for AI coding agents, enabling them to read and write structured knowledge through MCP-compatible tools.Last updated1MIT
- Alicense-qualityBmaintenanceProvides persistent, local-first AI memory across sessions via MCP tools for storing, searching, and retrieving context from past interactions.Last updated1MIT
- Alicense-qualityBmaintenanceProvides AI agents with persistent knowledge storage, enabling them to store, search, and retrieve text, documents, and files using semantic and keyword search via MCP tools.Last updated31Apache 2.0
- AlicenseAqualityDmaintenanceProvides persistent memory with semantic search for MCP-based AI agents, enabling them to store and recall information across sessions using vector embeddings.Last updated41MIT
Related MCP Connectors
Shared long-term memory vault for AI agents with 20 MCP tools.
User-owned memory for AI agents, Copilot, Claude, IDEs, CLIs, and chat apps over remote MCP.
Persistent memory and knowledge management for AI agents with semantic search and 50+ tools.
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/cx2002302-lang/memory_plus'
If you have feedback or need assistance with the MCP directory API, please join our Discord server