Project Memory

Name: Project Memory
Author: Dankaro-projects

作者：Dankaro-projects版本 0.5.0b9i★ –i在你的电脑上运行

注册名称io.github.Dankaro-projects/project-memory

安装

概览

作者提供的说明（英文原文）

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

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。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

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？

ClaudeClaude CodeCursorVS CodeChatGPT

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": {
    "project-memory": {
      "command": "uvx",
      "args": [
        "project-memory-mcp",
        "serve",
        "--project",
        "{project_path}"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add project-memory -- uvx project-memory-mcp serve --project '{project_path}'

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": {
    "project-memory": {
      "command": "uvx",
      "args": [
        "project-memory-mcp",
        "serve",
        "--project",
        "{project_path}"
      ]
    }
  }
}

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": {
    "project-memory": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "project-memory-mcp",
        "serve",
        "--project",
        "{project_path}"
      ]
    }
  }
}

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 Dankaro-projects 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 7229 个服务器的 0%
发布到注册中心的时间比此分类中其他 7229 个服务器的 42% 更新
基础检查结果等于或优于此分类中其他 7229 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 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（0.5.0b9）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, 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。

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

Template	For	Starter documents	Phases
`product`	A software product	`docs/brief.md`, `docs/research.md`, `docs/architecture.md`, `docs/stories.md`	Goals and brief, Research, Requirements approval, Architecture, User stories and acceptance, Build, Review and release
`engagement`	A consulting engagement	`engagement/brief.md`, `engagement/stakeholders.md`, `engagement/research.md`, `engagement/findings.md`, `deliverables/README.md`	Scope and brief, Stakeholders and hypotheses, Research and evidence, Analysis and synthesis, Recommendations and deliverables, Client review, Handover
`automation`	A workflow automation, with n8n exports in `workflows/`	`automation/process.md`, `automation/systems.md`, `automation/solution-design.md`, `workflows/README.md`	Process 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.

View	Content
Now	One 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
Plan	The 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
Work	A table and a board of work items, and a page for each with the plan, the allowed paths, dependencies, agent runs, lineage and history
Architecture	Components from source code, exported n8n workflows and authored items such as systems, stakeholders and deliverables
Dependencies	The work dependency graph with blocked chains, and the declared package table
Decisions	Decisions with their outcomes, and the lineage from requirement to outcome
Learning	Accepted guards and their recurrences, lessons awaiting your acceptance, failures without a lesson and scope widenings
Agents	Host availability, agent checks, delegated runs, and the merge or discard decision
Records	Search across every record kind, captured documents and host receipts
Requirements	The 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

User guide, from the first conversation to delivery
Control panel and agent checks and delegated work
Setup and lifecycle and record fields
Testing and limitations, contributing, security and releasing

Packages are published on PyPI and in GitHub Releases.

Project Memory

概览

PopNx MCP 的三大支柱

它能帮你做什么

安装

安装之前

它能做什么

基础检查i

作者与仓库一致

仍在维护

有许可证

端点有响应

使用 OAuth 登录

工具描述中没有隐藏指令

与“搜索与 AI 记忆”中的其他服务器比较i

常见问题

替代选择

Context7

Codebase Memory

Exa

作者的 README

Project Memory

Install

Start a project

The kickoff conversation

The everyday loop

The control panel

Read the records in Obsidian

Agent checks and delegated work

Guards and scope

Honest boundaries

Documentation