Project Memory

Name: Project Memory
Author: Dankaro-projects

제작자: Dankaro-projects버전 0.5.0b9i★ –i내 컴퓨터에서 실행

레지스트리 이름io.github.Dankaro-projects/project-memory

설치

개요

제작자가 쓴 설명(영어 원문)

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

PopNx MCP의 세 기둥

이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.

사용자 의견i아직 리뷰가 없어요리뷰는 다음 단계에서 계정 기능과 함께 열려요
우리의 기본 점검i1개 중 1개 통과공개된 정보로 점검했으며, 코드는 아직 스캔하지 않았어요
사용 정도i아직 측정 안 함★ –

할 수 있는 일

이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.

연결 방식: 내 컴퓨터에서 실행
전송 방식: stdio
카테고리: 검색 및 AI 기억

설치

쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.

설치하기 전에

기본 점검: 1개 중 1개 통과
내 컴퓨터에서 내 사용자 계정의 권한(파일, 네트워크, 앱)으로 실행돼요.
레지스트리에 입력해야 할 키가 나와 있지 않아요.

필요한 것

PyPIPython의 uv 설치(uvx 명령어용)

어떤 앱을 쓰세요?

ClaudeClaude CodeCursorVS CodeChatGPT

위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.

Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요

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

터미널에서 이 명령어를 실행하세요

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

Cursor에서 열기

또는 설정을 직접 복사하기

프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요

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

브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.

VS Code에서 열기

또는 설정을 직접 복사하기

프로젝트의 .vscode/mcp.json에 붙여 넣으세요

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

브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.

이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.

선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.

이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.

할 수 있는 일

내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.

기본 점검i

누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 6일 점검

✓ 통과
제작자와 저장소 일치
레지스트리 이름이 GitHub 계정 Dankaro-projects 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
… 아직 점검 안 함
계속 관리되고 있음
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
… 아직 점검 안 함
라이선스 있음
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
– 해당 없음
엔드포인트 응답
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
– 해당 없음
OAuth로 로그인
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
… 아직 점검 안 함
도구 설명에 숨겨진 지시 없음
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요

검색 및 AI 기억 카테고리의 다른 서버와 비교i

이 카테고리의 다른 서버 7234개 중 0%보다 스타가 많아요
이 카테고리의 다른 서버 7234개 중 42%보다 최근에 레지스트리에 공개됐어요
기본 점검 결과가 이 카테고리의 다른 서버 7234개 중 100%와 같거나 더 좋아요

레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.

자주 묻는 질문

Project Memory, 안전한가요?

해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.

어떤 키나 코드가 필요한가요?

레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.

내 컴퓨터에서 실행되나요, 클라우드에서 실행되나요?

내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).

아직 관리되고 있나요?

GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.5.0b9)은 2개월 전 레지스트리에 올라왔어요.

어떤 앱에서 쓸 수 있나요?

Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Python (uv)도 있어야 해요.

대신 쓸 수 있는 건 뭐가 있나요?

같은 카테고리에는: Context7, Codebase Memory, Exa

대안

같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.

카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i

Context7

제작자: upstash

Up-to-date docs for AI agents

컴퓨터 또는 클라우드★ 64,224MIT3일 전 업데이트

설치

Codebase Memory

제작자: DeusData

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

내 컴퓨터에서 실행★ 48,148MIT20일 전 업데이트

설치

Exa

제작자: exa-labs

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

클라우드에서 실행★ 5,213MIT44일 전 업데이트

설치

제작자의 README

README 열기

제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.

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

개요

PopNx MCP의 세 기둥

할 수 있는 일

설치

설치하기 전에

할 수 있는 일

기본 점검i

제작자와 저장소 일치

계속 관리되고 있음

라이선스 있음

엔드포인트 응답

OAuth로 로그인

도구 설명에 숨겨진 지시 없음

검색 및 AI 기억 카테고리의 다른 서버와 비교i

자주 묻는 질문

대안

Context7

Codebase Memory

Exa

제작자의 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