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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Projectmem

作者:riponcm版本 0.3.5i★ –i在你的电脑上运行
注册名称io.github.riponcm/projectmem

安装

概览

作者提供的说明(英文原文)

Coding agent memory — one local MCP server for every project. Warns before repeating failed fixes.

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: 搜索与 AI 记忆

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)。
  • 注册中心没有列出需要填写的密钥。

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": {
    "projectmem": {
      "command": "uvx",
      "args": [
        "projectmem"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add projectmem -- uvx projectmem

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": {
    "projectmem": {
      "command": "uvx",
      "args": [
        "projectmem"
      ]
    }
  }
}

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": {
    "projectmem": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "projectmem"
      ]
    }
  }
}

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。

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6日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 riponcm 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“搜索与 AI 记忆”中的其他服务器比较i

  • 星标数多于此分类中其他 7226 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 7226 个服务器的 85% 更新
  • 基础检查结果等于或优于此分类中其他 7226 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

Projectmem 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(0.3.5)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:Context7, Codebase Memory, Exa

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

Context7

作者:upstash

Up-to-date docs for AI agents

电脑或云端★ 64,224MIT3天前更新
安装

Codebase Memory

作者:DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

在你的电脑上运行★ 48,148MIT20天前更新
安装

Exa

作者:exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

在云端运行★ 5,213MIT44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。

🎉 v0.3.5 is out — precheck recognises a file however its path is spelled, and the summary stays small even when entries are long. See what changed →

We don't make AI smarter. We make it experienced. Coding agent memory — the local-first memory + judgment layer for AI coding agents. One MCP server for every project. Stop re-reading your project every session. Stop repeating yesterday's bug.

Website • Guide • Demo • Changelog • Paper

https://github.com/user-attachments/assets/e055988e-7253-4ba9-92fc-95d3d4bcd763

pjm visualize — every case your project solved, what failed on the way, and what it saved. Generated locally from .projectmem/events.jsonl .


## 🚀 Start here — five minutes, once

*Five minutes if you follow along here. Want to be shown instead — every

command, the exact output it prints back, and the dashboards at the end?

Take [the complete setup guide](https://projectmem.dev/blog/projectmem-complete-guide-ai-coding-agent-memory).*

New to projectmem, or upgrading from 0.1.x / 0.2.x? Since 0.3.0 one MCP

server serves every project, so this is the last time you configure anything.

1. Install or update

`bash

pip install -U projectmem

`

2. Find the projects you already have

`bash

pjm doctor

`

It looks where code lives — ~/Developer, ~/code, ~/projects, your cloud

folders, and every drive on Windows — and lists projects with memory that

aren't registered yet. Anything it missed, add by hand:

`bash

pjm project register "/Users/you/Developer/repos/ossdrop"

`

3. Register them

`bash

pjm doctor --fix

`

4. Point your AI at all of them with one config

`json

"mcpServers": {

"projectmem": {

"command": "/absolute/path/to/python",

"args": ["-m", "projectmem.mcp_server"]

}

}

`

No --root, no cwd — that's what makes it serve everything. Per-client

instructions (Claude Desktop, Claude Code, Cursor, Antigravity, Codex) are in

MCP Integration; pjm init prints this block

with your own Python path filled in. Then fully restart the client — MCP

servers only load on a cold start.

5. Check your work

`bash

pjm doctor

`

Add --online if you also want it to tell you when a newer projectmem is out

— projectmem makes no network calls otherwise, and --auto turns that into a

once-a-day check if you prefer.

Run it again after editing the config. It flags any client still pinned to a

single repo — the most common reason a new project is invisible to your agent.

All green? You're done. From here on it is one command per repo:

`bash

pjm init

`

Your agent reads what the project already learned instead of rediscovering it,

and writes down what it finds. Fewer tokens, no repeated dead ends, memory that

outlives the session.


What is coding agent memory?

Coding agent memory is a persistent record of what happened while building a project — the issues hit, the approaches attempted, the fixes that worked and the decisions made — stored so an AI coding agent can read it at the start of a new session. Without it every session begins from zero.

projectmem is an open-source agent memory layer built for that job. It is local-first: memory lives in a plain .projectmem/ directory inside your repository, with no cloud, no account and no telemetry — the only network call it can make is an update check you turn on yourself. A native MCP server exposes 17 tools to Claude Code, Claude Desktop, Cursor, Antigravity and Codex, so your agent reads memory and logs its work on its own.

Unlike chat-history memory tools, projectmem stores typed events — issues, attempts, fixes, decisions, notes — which is what makes its signature feature possible: a pre-commit warning that fires *before* you repeat an approach that already failed in this project.

pip install projectmem

cd your-project && pjm init


🎬 Watch the demo

Full screen-recorded tutorial- watch on YouTube

📚 Docs

DocWhat's in it
[Complete setup guide](https://projectmem.dev/blog/projectmem-complete-guide-ai-coding-agent-memory)The full walkthrough on the web — install, MCP setup per client, pjm doctor, your first logged issue, and both dashboards. Every terminal output is captured from a real run.
[TUTORIAL.md](https://github.com/riponcm/projectmem/blob/main/TUTORIAL.md)15-minute step-by-step walkthrough — set up projectmem on your own project, watch the lifecycle, see the pre-commit warning fire.
[CHANGELOG.md](https://github.com/riponcm/projectmem/blob/main/CHANGELOG.md)Release history. Latest: v0.3.5 — precheck matches every spelling of a path, and long entries no longer bloat the summary.
[Research paper (arXiv:2606.12329)](https://arxiv.org/abs/2606.12329)*PROJECTMEM: A Local-First, Event-Sourced Memory and Judgment Layer for AI Coding Agents* — the peer-readable version: design, Memory-as-Governance framing, capability comparison, and the 207-event dogfooding study.
[LICENSE](https://github.com/riponcm/projectmem/blob/main/LICENSE)MIT

The Problem

Every new AI session starts from zero. Claude, Cursor, Aider — they all forget yesterday's decisions, repeat failed debugging attempts, and burn millions of tokens reconstructing context from raw source files.

The model isn't the problem. The architecture is. Stateless models need a memory cortex.

The Solution

projectmem is the local-first memory + judgment layer that sits above your AI tools. It captures every failed attempt, decision, and gotcha — then injects that experience back into future AI sessions. Git tracks *what* changed. projectmem tracks *why* it changed, what was tried, and what failed.

Install

First time here? → [The complete setup guide](https://projectmem.dev/blog/projectmem-complete-guide-ai-coding-agent-memory)

walks the whole path end to end: install, connecting Claude Desktop, Claude Code,

Cursor, Codex or Antigravity, checking it with pjm doctor, and reading your

memory back through the dashboards — with the real terminal output at every step.

Three commands to a project that remembers:

pip install projectmem

cd your-project pjm init

That's it. pjm init installs three git hooks (pre-commit warnings, post-commit classification, post-merge tracking), auto-starts a real-time file watcher, inherits cross-project memory if available, and creates .projectmem/. Capture is active from minute one.

The canonical command is projectmem. A pjm alias is installed for speed.


✨ New in 0.3.5 — warnings for every spelling, and a summary that stays small

Precheck recognises a file however its path is spelled. Events store project-relative paths, so asking about ./src/auth.py, an absolute path, a path through a symlinked folder or a Windows C:\ path matched nothing, and precheck called a file with known failures "Safe to modify". Every spelling now gets the same warnings, and checking is faster on large logs. get_issue("1") and get_issue("#1") find issue 0001. (#26)

Long entries no longer bloat the summary. 0.3.4 capped how many entries the summary shows, not how long each one is. Records are now clipped at 300 characters, and a fixed issue takes one line. On a real 238-event project the summary went from 8,974 tokens to 3,458. (#29, #30)

Upgrading? Run `pjm init` once in each project. The instructions now ask agents for shorter records, and pjm init refreshes AI_INSTRUCTIONS.md, keeping your previous copy as AI_INSTRUCTIONS.md.bak.

The #26 fixes were contributed by @vishalhabib99, and #29 was reported and fixed by @bigfree.

✨ New in 0.3.4 — memory that stays small, and warnings you can trust

The summary no longer grows forever. Notes were capped, but Recent issues and Decisions listed everything ever logged, so on an aged project reading the memory cost more than reading the code. Every open issue is still shown — the newest 20 in full, older ones by id — while fixed issues are capped at the newest 10 and decisions at the newest 15, each with a line saying what was left out and where to find it. On a test project with 25 issues and 60 decisions the summary went from about 2,900 tokens to about 1,100. (#19)

Commits are filed under the code they change. Auto-capture used the first changed path in git's sort order, which was usually the regenerated .projectmem/summary.md — so valid memories were flagged stale and the real file lost its history. The location is now the source file the commit is about, with deletions, renames, merges and a repository's first commit handled. (#20)

Stale warnings no longer depend on your time zone. A 0.3.3 speed-up compared git's local-time commit dates with UTC event times as text: west of UTC, recent stale memories were never flagged; east of UTC, fresh ones were flagged at once. Both sides are compared in UTC now, and events already filed under .projectmem/ stop warning without your log being rewritten.

*

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