原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
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Memdex

作者:ChoYugyeong版本 0.3.1i★ –i在你的电脑上运行
注册名称io.github.ChoYugyeong/memdex

安装

概览

作者提供的说明(英文原文)

Shared memory for AI coding agents: summaries, symbol map, notes and agent-to-agent messages.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 文档、表格与办公

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 需要你自己填写: MEMDEX_AGENT, MEMDEX_HOME

你需要

  • PyPI已安装 Python 的 uv(用于 uvx 命令)

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "memdex": {
      "command": "uvx",
      "args": [
        "memdex-mcp"
      ],
      "env": {
        "MEMDEX_AGENT": "your-value",
        "MEMDEX_HOME": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add memdex --env MEMDEX_AGENT=your-value --env MEMDEX_HOME=your-value -- uvx memdex-mcp

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "memdex": {
      "command": "uvx",
      "args": [
        "memdex-mcp"
      ],
      "env": {
        "MEMDEX_AGENT": "your-value",
        "MEMDEX_HOME": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "memdex": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "memdex-mcp"
      ],
      "env": {
        "MEMDEX_AGENT": "your-value",
        "MEMDEX_HOME": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上,无需账户。

需要你填写的值

  • MEMDEX_AGENT env
    Name this agent uses in notes, messages and claims (claude, codex, cursor, ...).
  • MEMDEX_HOME env
    Directory holding memdex.db (default ~/.memdex).

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。

它能做什么

它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 ChoYugyeong 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    它在你的电脑上运行;我们只能读取互联网端点的工具列表

与“文档、表格与办公”中的其他服务器比较i

  • 星标数多于此分类中其他 2198 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 2198 个服务器的 81% 更新
  • 基础检查结果等于或优于此分类中其他 2198 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。

常见问题

Memdex 安全吗?

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。

它需要哪些密钥或代码?

注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端?

它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(0.3.1)于6天前进入注册中心。

哪些应用可以使用它?

支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。

有什么可以替代它?

同一分类中有:Agent-Native Calendar, Excel MCP Server, MCP Server for Excel

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Agent-Native Calendar

作者:BuilderIO

Agent-Native Google Calendar - manage events, sync, and public booking

在云端运行★ 7,620MIT2个月前更新
安装

Excel MCP Server

作者:haris-musa

Create and edit Excel files with AI: charts, PivotTables, formulas. No Excel needed.

在你的电脑上运行★ 4,276MIT7天前更新
安装

MCP Server for Excel

作者:sbroenne

Excel automation for AI - Sheets, Power Query, DAX, VBA, Tables, Ranges and more. Windows only.

在你的电脑上运行★ 847MIT4天前更新
安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

memdex

Shared, token-saving memory for AI coding agents.

One SQLite file. Zero dependencies. Any MCP client. Claude Code, Codex, Cursor and DeepSeek can all remember the same things — and talk to each other.

Quick start · Adding agents · How it works · Tools · CLI · Configuration · Comparison · FAQ · 한국어


memdex is the memory coding agents share. One SQLite file, zero dependencies, no cloud. A session starts with what the last one left — summaries, decisions, messages, handoffs — instead of re-reading the same 3,000-token files and re-discovering the same pitfalls, while several agents work one repo without stepping on each other's edits.

pip install memdex-mcp        # PyPI name is memdex-mcp; the command is `memdex`

memdex connect # wires claude, codex, cursor, vs code, chatgpt, … (serve-check included) memdex brief # what the last session left for you — run it now

  • A file summary written once stays valid until the file's content changes. Summaries are keyed to the sha256 of the content, not the path. The next session — or the next agent — gets a 100-token summary instead of the 3,000-token file, and is told the moment it goes stale.
  • Notes know when they rot. Decisions, pitfalls and handoffs cite the files they're based on; when those files change, the note is flagged.
  • Agents coordinate. claim what you're editing, post to other agents, read your inbox, leave a handoff for whoever comes next. All worktrees of a repository share one memory.

On a real 66-file site repo, memdex bench measures 154,105 tokens to read every file versus 3,471 tokens of outlines — 98% smaller, before an agent saves a single summary. Run memdex bench in your own checkout; the token count is chars / 4, an estimate, and the ratio is the point.

Listed in the official MCP Registry as io.github.ChoYugyeong/memdex and on PyPI as memdex-mcp.

Quick start

pipx install memdex-mcp        # PyPI name is memdex-mcp; the command is `memdex`

memdex connect --agent claude # write the MCP entry into the agents on this machine

Claude Code (user scope, every project) — when the claude CLI is on PATH, memdex connect runs this line for you, and prints it when it is not:

claude mcp add memdex --scope user -e MEMDEX_AGENT=claude -- memdex serve

Everything else that keeps a config file — Claude Desktop, Cursor, VS Code (GitHub Copilot), Windsurf, Zed, Cline, Gemini CLI, Codex CLI, and DeepSeek Harness: memdex connect finds each one that is already installed, merges mcpServers.memdex into its config (servers.memdex with type: stdio for VS Code, context_servers.memdex for Zed, [mcp_servers.memdex] for Codex), preserves other settings, and saves a .bak before it writes. For DeepSeek Harness it adds a marked @deepseek-ai/dsh-mcp-client entry to each existing profile's cordis.patch.yml; only that managed block is replaced or removed. A client whose config folder does not exist is reported as not installed and left alone; an unreadable or malformed JSON/TOML config is left exactly as it was, and a Zed settings file with comments gets the block printed to paste instead of being rewritten (a rewrite would drop the comments). For DSH, a non-list root or a manual ID collision is also left untouched. --list shows what is installed and configured without writing, --dry-run shows the writes, --remove takes the entry out.

For DeepSeek Harness, run memdex connect --client deepseek-harness --agent deepseek --serve-check, then start a new Harness session. --serve-check verifies Memdex itself answers MCP initialize and tools/list; it does not claim the Harness loaded the plugin. Confirm the mcp__memdex__... tools appear in that session and call project_brief before delegating. The MCP entry runs the same Python package that ran connect, with the profile's current working directory as Memdex's project scope. A profile with a hand-written entry using id: memdex-memory, or a patch that is not a top-level YAML list, is left untouched for manual review.

If more than one Harness profile exists, connect stops before writing and asks for an explicit target: memdex connect --client deepseek-harness --profile desktop --agent deepseek --serve-check. The profile is the directory name under ~/.dsh/profiles (or $DSH_HOME/profiles); memdex connect --list shows the paths. This avoids silently adding the MCP server to every profile that happens to be installed.

`--serve-check` is the honest end of setup: after writing, memdex starts the server from the entry it just wrote and speaks MCP to it (initialize, then tools/list), so a wrong path, a missing interpreter or a database it cannot open is caught now instead of in the agent:

+ serve-check      memdex answered: 15 tools, protocol 2024-11-05

Any model, MCP or not — MCP clients join through memdex connect; a model that can only run shell commands joins with nothing to configure. Both paths are in Adding agents.

Team scope — put the config in the repo so everyone who clones it gets memdex:

memdex connect --project     # .vscode/mcp.json and .mcp.json, written with a bare `memdex` command

A committed config cannot carry this machine's absolute path, so project scope writes command: "memdex" and the teammate's PATH has to find it (pipx install memdex-mcp). With the claude CLI on PATH and --project, the CLI writes .mcp.json itself (claude mcp add memdex --scope project …) and memdex writes only the VS Code file, so one file never has two owners. --no-claude skips the CLI entirely, and --client claude-code runs only that step.

By hand, or for a client memdex does not know about:

{ "mcpServers": { "memdex": { "command": "memdex", "args": ["serve"], "env": { "MEMDEX_AGENT": "codex" } } } }

Give each agent its own MEMDEX_AGENT name; its id becomes name@checkout (e.g. claude@myrepo-wt). memdex config --agent prints the snippets above, plus an optional Claude Code SessionStart hook that injects the project brief automatically.

That's it. The server sends its own usage instructions in the MCP initialize response, so agents know the workflow (brief → file_summary before reading → remember → handoff), and when to use the task ledger for substantial work, without extra prompting.

Adding agents

Every agent — MCP client or not — joins the same loop in the same four steps:

  1. Name it. MEMDEX_AGENT becomes its id (name@checkout). Two agents sharing one name cannot see each other's messages, so each gets its own.
  2. Wire it. MCP clients: one command (below). A model that can only run shell commands: nothing.
  3. Verify. memdex doctor (✓ clients), codex mcp list for Codex, memdex connect --serve-check for the MCP handshake, memdex whoami for anything.
  4. Work. Every agent, however it joined, uses the same basic loop: brief, claim, post/inbox, handoff. For substantial work with independently omittable outcomes, add a tasks tree before execution so blockers and incomplete branches stay visible.

MEMDEX_AGENT is a self-declared label, not an authenticated identity. A verified task means a different label recorded a peer check; it is a useful coordination signal, not cryptographic proof of who checked it.

For a long-running watcher, inbox --follow --poll 2 --notify-every 900 checks for messages every two seconds without consuming them, then prints a batch every 15 minutes. It flushes output before acknowledging the cursor; if the process stops between those steps, a numbered message may appear again, but is not lost. If a scheduler starts a fresh process on every check, use inbox --notify-every 900 instead. Its digest window is persisted per project and agent: the first check with unread messages opens a 15-minute window, later polls keep checking without consuming them, and due polls deliver the accumulated batch. Use the same agent id, project path, and interval on each run. The MCP inbox tool accepts the same notify_every value.

Agent / modelHow it joinsVerify with
Claude Codememdex connect (runs claude mcp add …), plus the SessionStart hookclaude mcp list
Codex CLImemdex connect → [mcp_servers.memdex] in ~/.codex/config.tomlcodex mcp list
DeepSeek Harnessmemdex connect --client deepseek-harness --agent deepseek --serve-checknew session, then call project_brief
Claude Desktop, Cursor, VS Code, Windsurf, Zed, Cline, Gemini CLImemdex connect writes their config, one key eachmemdex doctor
ChatGPT Desktopapp UI: Settings → MCP → add server (command memdex, args serve, env MEMDEX_AGENT=gpt); after the file exists, memdex connect manages itthe app's MCP panel
Any model that can run shell commands — a local LLM, a wrapper script, another harnessnothing to wire: set MEMDEX_AGENT and use the coordination commandsmemdex whoami
# a model with no MCP client, joining in ten seconds

MEMDEX_AGENT=codex memdex brief # what the last session left for you MEMDEX_AGENT=codex memdex claim src/store.py --intent "add retry" MEMDEX_AGENT=codex memdex task create --title "Retry support" --criterion "Retries are bounded and verified" MEMDEX_AGENT=codex memdex task create --parent 1 --title "Implement retry" --criterion "Retry path works" --owner codex MEMDEX_AGENT=codex memdex post "Retry work is underway" --group --to claude --task 1 MEMDEX_AGENT=codex memdex handoff "retry done" --next "wire timeout"

memdex connect --project puts the config in the repo so teammates get memdex on clone.

Sending to a specific chat

In the intended DeepSeek chat's shell, run memdex session routes --label "routing review". The CLI derives a project-local address from the checkout, agent and host conversation ID (DSH_SESSION_ID in DeepSeek or CODEX_THREAD_ID in Codex). Point both sides at the same project with `--path <che

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