Agent-Native Calendar
Agent-Native Google Calendar - manage events, sync, and public booking
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.
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。
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。
选择你使用的 AI 应用,然后复制设置。
MEMDEX_AGENT, MEMDEX_HOME你需要
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。
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。
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。
MEMDEX_AGENT envMEMDEX_HOME env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。
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
注册名称已通过 GitHub 账户 ChoYugyeong 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
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。
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。
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)。
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。
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)。
同一分类中有:Agent-Native Calendar, Excel MCP Server, MCP Server for Excel
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 Google Calendar - manage events, sync, and public booking
Create and edit Excel files with AI: charts, PivotTables, formulas. No Excel needed.
Excel automation for AI - Sheets, Power Query, DAX, VBA, Tables, Ranges and more. Windows only.
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
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.
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.
Every agent — MCP client or not — joins the same loop in the same four steps:
MEMDEX_AGENT becomes its id (name@checkout). Two agents sharing one name cannot see each other's messages, so each gets its own.memdex doctor (✓ clients), codex mcp list for Codex, memdex connect --serve-check for the MCP handshake, memdex whoami for anything.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 / model | How it joins | Verify with |
|---|---|---|
| Claude Code | memdex connect (runs claude mcp add …), plus the SessionStart hook | claude mcp list |
| Codex CLI | memdex connect → [mcp_servers.memdex] in ~/.codex/config.toml | codex mcp list |
| DeepSeek Harness | memdex connect --client deepseek-harness --agent deepseek --serve-check | new session, then call project_brief |
| Claude Desktop, Cursor, VS Code, Windsurf, Zed, Cline, Gemini CLI | memdex connect writes their config, one key each | memdex doctor |
| ChatGPT Desktop | app UI: Settings → MCP → add server (command memdex, args serve, env MEMDEX_AGENT=gpt); after the file exists, memdex connect manages it | the app's MCP panel |
| Any model that can run shell commands — a local LLM, a wrapper script, another harness | nothing to wire: set MEMDEX_AGENT and use the coordination commands | memdex 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.
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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