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PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

Project Memory

作者:Dankaro-projects版本 0.5.0b9i★ 0i在你的电脑上运行
注册名称io.github.Dankaro-projects/project-memory

安装方法

What it does

Local decisions, evidence, outcomes and reviewed lessons with bounded retrieval and an HTML viewer.

  • Connects 在你的电脑上运行
  • Account or key No key needed
  • Category 搜索与 AI 记忆

安装

选择你使用的 AI 应用,然后复制设置。

通过 3/3 项检查 · 在你的电脑上运行

第一步:你用哪个 AI?

选择你用的应用,步骤会显示在下方。

可连接 Claude,但需要先准备

需要先在电脑上安装 Python (uv)(只需一次) · 仅限电脑上的 Claude 应用(Windows 或 Mac),网页和手机不可用

  1. 安装 Python (uv)

    只需一次,所有需要 Python (uv) 的服务器都能用

    打开 Python (uv) 下载页 ↗

  2. 打开电脑版 Claude 应用的设置文件

    SettingsDeveloperEdit Config

  3. 粘贴这些设置并保存文件

    正在检查命令 · 通过后才会显示复制按钮

  4. 退出 Claude 再重新打开

  5. 试一试

    用 Project Memory 告诉我你能帮我做什么,然后做一件给我看

    ✓ 如果 Claude 说正在使用 Project Memory,或回答中出现 Project Memory 的工具名称,就表示连接成功

菜单名称已于 2026年11月11日 对照 Anthropic 帮助页面核实 · 你的界面可能因版本略有不同

ChatGPT 暂时无法连接 Project Memory

Project Memory 需要安装在你的电脑上,而 ChatGPT 只能连接在网上运行的服务器

你可以改用这些方式

A · 使用能连接 Project Memory 的应用

  • 需要先在电脑上安装 Python (uv)(只需一次) · 仅限电脑上的 Claude 应用(Windows 或 Mac),网页和手机不可用
  • 需要先在电脑上安装 Python (uv)(只需一次)
  • 需要先在电脑上安装 Python (uv)(只需一次)
  • 需要先在电脑上安装 Python (uv)(只需一次)

B · 搜索与 AI 记忆 类中能连接 ChatGPT 的其他服务器

从同类中使用较多的里挑选,仅限用地址即可连接、无需密钥的

可连接 Claude Code,但需要先准备

需要先在电脑上安装 Python (uv)(只需一次)

  1. 安装 Python (uv)

    只需一次,所有需要 Python (uv) 的服务器都能用

    打开 Python (uv) 下载页 ↗

  2. 打开终端

    Mac:按 Command+空格,输入 Terminal · Windows:打开 PowerShell

  3. 粘贴这条命令并按 Enter

    正在检查命令 · 通过后才会显示复制按钮

  4. 在 Claude Code 中输入 /mcp

    应能看到 Project Memory 在列表中并显示已连接

  5. 试一试

    用 Project Memory 告诉我你能帮我做什么,然后做一件给我看

    ✓ 如果 Claude Code 说正在使用 Project Memory,或回答中出现 Project Memory 的工具名称,就表示连接成功

菜单名称已于 2026年11月11日 对照 Anthropic 帮助页面核实 · 你的界面可能因版本略有不同

可连接 Cursor,但需要先准备

需要先在电脑上安装 Python (uv)(只需一次)

  1. 安装 Python (uv)

    只需一次,所有需要 Python (uv) 的服务器都能用

    打开 Python (uv) 下载页 ↗

  2. 点击这个按钮打开 Cursor

    正在检查命令 · 通过后才会显示复制按钮

  3. 在 Cursor 中以 Agent 模式打开 AI 聊天

  4. 试一试

    用 Project Memory 告诉我你能帮我做什么,然后做一件给我看

    ✓ 如果 Cursor 说正在使用 Project Memory,或回答中出现 Project Memory 的工具名称,就表示连接成功

菜单名称已于 2026年11月11日 对照 Cursor 帮助页面核实 · 你的界面可能因版本略有不同

可连接 VS Code,但需要先准备

需要先在电脑上安装 Python (uv)(只需一次)

  1. 安装 Python (uv)

    只需一次,所有需要 Python (uv) 的服务器都能用

    打开 Python (uv) 下载页 ↗

  2. 点击这个按钮打开 VS Code

    正在检查命令 · 通过后才会显示复制按钮

  3. 在 VS Code 中以 Agent 模式打开 AI 聊天

  4. 试一试

    用 Project Memory 告诉我你能帮我做什么,然后做一件给我看

    ✓ 如果 VS Code 说正在使用 Project Memory,或回答中出现 Project Memory 的工具名称,就表示连接成功

菜单名称已于 2026年11月11日 对照 VS Code 帮助页面核实 · 你的界面可能因版本略有不同

其他支持 MCP 的应用(如 Windsurf、Zed、Gemini CLI)添加方式各不相同,请在应用列表中查看

查看所有支持 MCP 的应用 →

JSON 设置在下方“开发者专用”中

卡住了?问问正在用 Project Memory 的人💬 讨论 →
开发者专用:终端命令和 JSON 设置

安装在电脑上

Claude Code

正在检查命令 · 通过后才会显示复制按钮

Claude Desktop (claude_desktop_config.json)

正在检查命令 · 通过后才会显示复制按钮

Cursor (~/.cursor/mcp.json)

正在检查命令 · 通过后才会显示复制按钮

VS Code (mcp.json)

正在检查命令 · 通过后才会显示复制按钮

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。

一览

每周下载量i
96/周
连接方式
在你的电脑上运行
最新版本i
0.5.0b9
发布到注册中心i
2026年9月15日
代码最后更新i
2026年10月8日
作者
Dankaro-projects
软件包
pypi: project-memory-mcp
注册状态
活跃

哪些应用可以使用它

依据这个服务器的连接方式和各应用自己的文档,检查于 2026年11月10日。

在你的电脑上运行,适用于

ClaudeGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatRaycast

全部应用及其优缺点 →

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13日

  • ✓ 通过

    作者与仓库一致

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

  • ✓ 通过

    仍在维护

    代码最后更新于36天前

  • ✓ 通过

    有许可证

    使用 MIT

  • – 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

讨论

提问、分享技巧或报告无法使用。按“有帮助”数量排序。

还没有帖子,欢迎第一个提问或分享。

此处内容由用户撰写,不代表 PopNx 团队。

使用 Google 登录后发帖

用户评价

还没有评价

用 Google 登录后写评价

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 6047 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 6047 个服务器的 39% 更新
  • 基础检查结果等于或优于此分类中其他 6047 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

Project Memory 安全吗?

它在 3 项适用的基础检查中通过了 3 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36天前,最新版本(0.5.0b9)于2个月前进入注册中心。

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, Ruflo

同类别的其他选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:↓ 每周下载量i★ 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

Up-to-date docs for AI agents

作者:upstash广泛使用↓ 125万/周★ 6.5万昨天更新

电脑或云端

Codebase Memory

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

作者:DeusData广泛使用↓ 1.7万/周★ 4.9万27天前更新

在你的电脑上运行

Ruflo

Agent teams, swarms, memory and hooks for Claude Code and Codex, served over MCP.

作者:ruvnet广泛使用↓ 7.4万/周★ 8.2万 整个仓库的星标今天更新

在你的电脑上运行

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。

Project Memory

Project Memory keeps the plan, the evidence, the decisions, the outcomes and the accepted lessons of one project in a local SQLite database. Your assistant reads and writes those records through three MCP tools. You follow the same records in a local control panel, which is read only, and you decide in the chat what is approved, accepted and merged.

It works for a software product, a consulting engagement and a workflow automation built in n8n. Work items can be code, documents, deliverables or exported workflows.

Public beta. Python 3.11 or newer is required. The runtime uses only the Python standard library. There is no telemetry, no hosted database and no account. Agent checks and delegated work run through the Codex or Claude Code CLI that you already have installed, and they consume that account's usage.

Install

Install uv, then run this inside the project you want to remember:

uvx project-memory-mcp@0.6.0b16 setup --client codex --trust

For Claude Code, replace --client codex with --client claude. Setup preserves existing records and settings, connects the MCP server and the lifecycle hooks, and opens the control panel. --trust approves the project integration in that client; omit it to approve the connection yourself. Start a new assistant session afterwards. Add --no-view for a headless installation.

For a permanent command:

uv tool install project-memory-mcp==0.6.0b16

project-memory doctor project-memory view

To install the published wheel directly from GitHub instead of PyPI:

uvx --from https://github.com/Dankaro-projects/project-memory/releases/download/v0.6.0b16/project_memory_mcp-0.6.0b16-py3-none-any.whl project-memory setup --client claude --trust

Other local MCP clients use setup --client mcp and the stdio server. Setup and lifecycle covers client configuration, the plugins, upgrades, backups and removal.

Start a project

A new project starts from one of three templates:

project-memory init ~/projects/pricing-tool --template product --client codex
TemplateForStarter documentsPhases
productA software productdocs/brief.md, docs/research.md, docs/architecture.md, docs/stories.mdGoals and brief, Research, Requirements approval, Architecture, User stories and acceptance, Build, Review and release
engagementA consulting engagementengagement/brief.md, engagement/stakeholders.md, engagement/research.md, engagement/findings.md, deliverables/README.mdScope and brief, Stakeholders and hypotheses, Research and evidence, Analysis and synthesis, Recommendations and deliverables, Client review, Handover
automationA workflow automation, with n8n exports in workflows/automation/process.md, automation/systems.md, automation/solution-design.md, workflows/README.mdProcess discovery, Systems and credentials inventory, Solution design, Workflow stories and test data, Build workflows, Test with sample data, Deployment and handover

init creates the folder, initialises git unless you pass --no-git, writes any starter document that does not exist yet, connects the clients you name and records each phase as a work item. Running it again creates nothing a second time and never overwrites a document you have edited.

The kickoff conversation

Each template carries seven kickoff questions. Your assistant reads them with memory_get kickoff and asks you, for example what problem the product solves, who the stakeholders are, or which systems the process uses. It records your answers with memory_write answer_kickoff, fills the starter documents from those answers and proposes requirements. Only you approve the requirements baseline. Until that approval exists, the control panel opens on a kickoff checklist that shows the open questions, the research still needed, the starter documents that are still empty and the phases.

The everyday loop

Tell the assistant the result you want, the constraints and what would count as complete. It then records a work item with its objective, its completion criterion, the next action and the files it may change, continues from that record in later sessions, and records each decision with its evidence, alternatives, uncertainty and expected consequence. After acting, it records what actually happened. A failure stays visible after a later success.

You approve requirements, accept or reject proposed lessons, widen the allowed paths, and decide what is merged. Capturing a document does not approve its contents, and a plan does not authorise work.

The control panel

Run project-memory view in the project, or ask the assistant to open Project Memory. The panel reads the same database and refreshes while it is open. It is laid out as a Notion workspace: a sidebar of views, database tables with views, filters, sorting and grouping, and each item opening as a page beside the list, in a light or a dark theme.

ViewContent
NowOne sentence on the project position, the kickoff checklist, and the work in progress, paused, ready and done with the next step of each, and recent decisions
PlanThe hierarchy of phases, epics, stories, research items, deliverables and workflows as an outline with nested sub-items and progress, and a gallery of the phases
WorkA table and a board of work items, and a page for each with the plan, the allowed paths, dependencies, agent runs, lineage and history
ArchitectureComponents from source code, exported n8n workflows and authored items such as systems, stakeholders and deliverables
DependenciesThe work dependency graph with blocked chains, and the declared package table
DecisionsDecisions with their outcomes, and the lineage from requirement to outcome
LearningAccepted guards and their recurrences, lessons awaiting your acceptance, failures without a lesson and scope widenings
AgentsHost availability, agent checks, delegated runs, and the merge or discard decision
RecordsSearch across every record kind, captured documents and host receipts
RequirementsThe current requirements, their version and their approval evidence

project-memory view --output review.html --include-bodies --no-open writes an offline snapshot instead. See the control panel guide.

Read the records in Obsidian

project-memory export --obsidian writes the records as a folder of Markdown notes with frontmatter and wikilinks, which Obsidian opens as a vault. A work item is a folder holding its own decisions, outcomes and plans, and sources, lessons and requirement revisions sit in shared folders beside it. The graph, the backlinks and the search of Obsidian then work on the project history, while the panel stays the place where you decide.

The export runs in one direction. The database is the source of truth, the export owns one folder inside the vault and touches nothing outside it, and a note edited inside that folder is lost on the next run. After a session that wrote records, the session stop hook refreshes the vault in a separate process.

Agent checks and delegated work

When Codex or Claude Code is configured, Project Memory can start short, separate host processes. An agent check reads the records and the project and reports on an outcome, on the current intent or on an unconfirmed execution. Delegated work runs a work item in its own git worktree, limited to the paths in its plan, and a second host reviews the resulting diff before you merge it. If one host reports a usage limit or a rate limit, the run reroutes once to the other configured host.

These runs use your installed Codex or Claude Code account and consume that account's usage. A reviewer cannot edit plans, accept lessons or merge anything. See agent checks and delegated work.

Guards and scope

An accepted lesson with a trigger becomes a guard. When a guard matches the paths or the wording of a work item, a decision must list that lesson in lessons_considered with an explicit yes or no and a reason, otherwise the decision is rejected. When a work item has allowed paths, the lifecycle hooks block an edit outside them, record the block and ask you to extend the scope in the chat. The record fields document defines the pattern rules.

Honest boundaries

  • A recorded plan is not permission. Autonomy and scope are advisory records; your host's own permissions and your current instructions decide what may run.
  • The scope guard reads the file paths of edit tools, patch markers and the targets of MCP write tools. It does not parse shell redirection or other indirect writes inside shell commands.
  • Hooks record events mechanically. Only the assistant can record what those events meant, and a connected panel does not prove that the recording is complete.
  • An agent verdict is an interpretation. A passing check does not prove that the work is correct, and an empty findings list is not proof either.
  • Architecture is read statically from imports, manifests and exported workflow files. Dynamic loading is not detected.
  • Bounded retrieval limits the characters a call returns. It cannot control the complete model input of the host.
  • Daily productivity, token usage, correction rates and human task time are not measured, and this project makes no claim about them. Testing and limitations states what is verified and what is not.

Documentation

Packages are published on PyPI and in GitHub Releases.