Personal Memory MCP Server

Personal Memory MCP Server

A self-hosted MCP server providing persistent memory for AI tools, allowing them to remember, recall, and manage information across sessions using a SQLite database.

Category
访问服务器

README

Personal Memory MCP Server

A small, self-hosted server that gives any MCP-compatible AI tool (Claude Desktop, Claude Code, and others that support MCP) a shared, persistent memory it can read and write — independent of any single app or project.

Storage is SQLite (one file, memory.db). Tools exposed: remember, recall, list_recent, forget, list_tags.


1. Run it locally first (sanity check)

pip install -r requirements.txt
export MEMORY_SERVER_TOKEN="pick-a-long-random-string-here"
python server.py

It starts on http://localhost:8000. Leave it running.


2. Deploy it somewhere reachable from all your devices

Pick one — all are low/no cost for personal use:

Option A: Railway (easiest)

  1. This repo is already on GitHub — good to go.
  2. Go to railway.app → New Project → Deploy from GitHub repo.
  3. In the service's Variables tab, add MEMORY_SERVER_TOKEN = your secret string.
  4. Railway auto-detects the Procfile and deploys. Add a Volume mounted at /data and set MEMORY_DB_PATH=/data/memory.db so your memories survive redeploys.
  5. Copy the generated public URL (e.g. https://your-app.up.railway.app).

Option B: Render

  1. New → Web Service → connect this repo.
  2. Build command: pip install -r requirements.txt. Start command: python server.py.
  3. Add env var MEMORY_SERVER_TOKEN. Attach a persistent disk at /data, set MEMORY_DB_PATH=/data/memory.db.

Option C: Any VPS (DigitalOcean, Hetzner, etc.) with Docker

docker build -t memory-server .
docker run -d -p 8000:8000 \
  -e MEMORY_SERVER_TOKEN="your-secret" \
  -v ~/memory-data:/data \
  memory-server

Put it behind a domain + HTTPS (e.g. Caddy or nginx + Let's Encrypt) so the token isn't sent over plain HTTP.


3. Connect Claude Desktop / Claude Code to it

Add a remote MCP server in Claude Desktop: Settings → Connectors → Add custom connector, with:

  • URL: https://your-deployed-url/mcp
  • Header: Authorization: Bearer your-secret-token

(Exact UI wording may vary by version — search Claude's docs for "custom connector" if the menu looks different.)

For Claude Code, add to your MCP config (e.g. .mcp.json or via claude mcp add):

{
  "mcpServers": {
    "personal-memory": {
      "url": "https://your-deployed-url/mcp",
      "headers": {
        "Authorization": "Bearer your-secret-token"
      }
    }
  }
}

Any other MCP-compatible client (some third-party tools support MCP connectors too) can point at the same URL + token to share the exact same memory store.


4. Using it

Once connected, just talk naturally:

  • "Remember that my Mayo outreach uses 'research fellow' not 'postdoc'."
  • "What have I saved about the heme-onc template?"
  • "Forget memory #7."

The AI tool decides when to call these tools based on your conversation — you don't need to invoke them manually.


Notes / limitations

  • Security: the bearer token is the only protection. Keep it secret, use HTTPS in production, and don't commit .env or memory.db to git (already in .gitignore).
  • Search is basic: recall does a plain SQL LIKE match, not semantic search. Fine for a personal memory store with dozens–hundreds of entries; if it grows large and keyword search stops finding things, this is the first place to upgrade (e.g. add embeddings + a vector column).
  • Backups: it's one SQLite file — back it up periodically (e.g. a cron job copying /data/memory.db somewhere safe) since most free hosting tiers don't guarantee volume durability.
  • This is separate from Claude's built-in memory — that still exists and works automatically within Claude. This server is for context you want to persist and be readable across different tools, not just Claude.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选