Context7
Up-to-date docs for AI agents
제작자가 쓴 설명(영어 원문)
Persistent memory for AI coding agents — past bugs, decisions, and your corrections, in your repo.
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
PROJECT_MEMORY_ROOT필요한 것
어떤 앱을 쓰세요?
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": "npx",
"args": [
"-y",
"@kaaustubh/project-memory-mcp"
],
"env": {
"PROJECT_MEMORY_ROOT": "your-value"
}
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add project-memory --env PROJECT_MEMORY_ROOT=your-value -- npx -y @kaaustubh/project-memory-mcp실행하기 전에 빈 값을 채워 주세요.
프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"project-memory": {
"command": "npx",
"args": [
"-y",
"@kaaustubh/project-memory-mcp"
],
"env": {
"PROJECT_MEMORY_ROOT": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"project-memory": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@kaaustubh/project-memory-mcp"
],
"env": {
"PROJECT_MEMORY_ROOT": "your-value"
}
}
}
}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.
PROJECT_MEMORY_ROOT env이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 7일 점검
레지스트리 이름이 GitHub 계정 kaaustubh 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
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.
레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(1.6.0)은 4개월 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Node.js도 있어야 해요.
같은 카테고리에는: Context7, Codebase Memory, Exa
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
A small, local MCP server that gives AI agents (Claude Code, Cursor, VS Code / GitHub Copilot, …) a shared, persistent memory of the projects in a code folder — what each project is, decisions made, and every bug/issue faced during development.
It is stateless: every tool reads/writes plain files on disk, so multiple clients (and multiple machines) share one source of truth.
| Layer | Lives in | Auto-loaded into context? | For |
|---|---|---|---|
| Project memory | /AGENTS.md | ✅ yes (via CLAUDE.md → @AGENTS.md) | identity, stack, run cmds, concise decisions/learnings — keep lean |
| Issue log | /issues.jsonl | ❌ no | high-volume bug/issue history — fetched on demand |
Design rule: durable, low-volume facts go in AGENTS.md (auto-loaded). High-volume history (bugs) goes in issues.jsonl (queried via search_issues). This keeps the always-loaded context small while keeping everything searchable.
Some orgs disable third-party MCP servers via policy (e.g. GitHub Copilot's MCP allowlist enforcement). Because the memory is plain files, not a service, the core value survives that:
AGENTS.md is auto-loaded by the editor reading it — no MCP call involved — so a project's identity, decisions, learnings, and preferences still land in the agent's context.io.github.kaaustubh/project-memory-mcp).Only the interactive tools (log_issue, search_issues, …) go over the MCP channel; the file-based memory keeps working without it.
list_projects, get_project, search_memory — read project memoryappend_decision, append_learning — append a dated bullet to AGENTS.mdremember_preference — turn a correction / stated habit into a remembered pattern (## Preferences in the root AGENTS.md for a global habit, or a project's for a local one); rides the auto-load, so it comes back next sessionlog_issue — record a bug/problem → issues.jsonlsearch_issues — "have we hit this before?" across all projects (field-scoped; optional tags filter)list_open_issues, resolve_issue — track / close bugssync_registry — reconcile the root AGENTS.md projects table with what's on disk (adds rows for new projects, flags stale ones)find_by_file — given a file path, surface the issues + decisions/learnings that touch it ("why is this code like this?")start_initiative, get_initiative, list_initiatives, update_initiative — track a named, multi-session effort (a codename, a plan, an evolving todo list) so it's resumable from any future session by name, not just within the one that started it; see Initiatives belowcheck_in, check_out — stamp the start/end of the working day and record a standup summary composed from evidence harvested off disk (git commits across every repo, uncommitted WIP, memory writes, initiative progress); the next morning's check_in hands the summary back — that's your standup. Print it any time, no agent needed: npx -y @kaaustubh/project-memory-mcp standupYou don't call these directly — you talk to your agent in natural language and it picks
the tool. See Using it day to day below for what to actually say.
Most of it runs itself: opening a project auto-loads its AGENTS.md (the agent already knows the project), and capture is proactive (plus the optional Stop hook). Your job is mainly to pull memory at the right moments. Just talk to your agent:
| When | Say something like | What fires |
|---|---|---|
| Before debugging anything | *"Have we hit this before? "* | search_issues across all projects |
| Starting something you've done elsewhere | *"How did I do Stripe webhook verification in any project?"* | search_memory (cross-project) |
| Landing on confusing code | *"Why is index.js like this? Check the memory."* | find_by_file |
| You made a real decision / fixed a real bug | *(nothing — it logs on its own and tells you)* | append_decision / log_issue |
| You correct how the agent works | *"No, always run the typecheck before committing — remember that."* | remember_preference (global or per-project) |
| Triage | *"What's still open across my projects?"* | list_open_issues |
| A bug is fixed | *"Resolve pulse_stripe-004 — fixed by …"* | resolve_issue |
| Added a new project | *"Sync the registry."* | sync_registry |
The one habit that matters: make *"have we hit this before?"* reflexive before every debugging session. That's where a memory tool earns its keep; the rest the system handles.
Capture is confirming, not silent — when the agent logs something it tells you in one line. Correct it freely: *"don't log that"*, or *"actually, log this too."*
Escape hatches: PROJECT_MEMORY_HOOK=off silences the Stop hook for one session; uninstall-hook removes it entirely.
From your code/projects folder, run:
cd ~/code # the folder that holds your projects
npx -y @kaaustubh/project-memory-mcp install
That registers the server, using the current directory as your projects root, with every client that has an MCP config location on this machine:
| Client | Config written |
|---|---|
| Claude Code | user scope, via claude mcp add |
| Cursor | ~/.cursor/mcp.json |
| VS Code / GitHub Copilot Chat | user-profile mcp.json (applies to every workspace) |
| GitHub Copilot CLI | ~/.copilot/mcp-config.json (or $COPILOT_HOME) |
| JetBrains Copilot plugin (IntelliJ, PyCharm, WebStorm, …) | ~/.config/github-copilot/intellij/mcp.json |
| Visual Studio (Windows) | %USERPROFILE%\.mcp.json — global, all solutions |
| Kimi Code CLI | ~/.kimi-code/mcp.json (or $KIMI_CODE_HOME) |
| Gemini CLI | ~/.gemini/settings.json |
| OpenAI Codex CLI | ~/.codex/config.toml (the only non-JSON client — merged as TOML) |
| Windsurf | ~/.codeium/windsurf/mcp_config.json |
Each write merges into the existing file (other MCP servers you've already configured are left alone) and is independently best-effort — a client that isn't installed on this machine is silently skipped, the rest still get registered. Restart whichever app(s) you use, then ask your agent *"set up project memory for this folder"* to scaffold AGENTS.md for each project.
Copilot surfaces (VS Code, CLI, JetBrains, Visual Studio): tools only run in
Agent mode, and config changes need a restart to take effect.
No clone, no global install — the MCP config just runs
npx, which fetches and runs
the latest version on demand.
Team memory (beta signup): want this memory shared across your team instead of
just your machine? Register your interest: https://github.com/kaaustubh/project-memory-mcp/issues/1
git clone https://github.com/kaaustubh/project-memory-mcp.git ~/code/.memory-server
cd ~/code/.memory-server && ./install.sh
A common question: *"once I install it, does it just start doing things?"* Not quite — the server is passive. Here's the actual flow:
AGENTS.md (via CLAUDE.md → @AGENTS.md) into the model's context for you. This is why the agent "just knows" what your project is — it's a built-in editor feature.Day one is empty. A fresh setup has no
AGENTS.mdfiles yet, so the auto-load has
nothing to load and
log_issuewill refuse until a project's memory exists. Bootstrap
once by asking your agent: *"set up project memory for this folder"* — it creates the
AGENTS.mdfiles. After that, everything works.
In short: a convention (auto-loaded files) + a tool the agent chooses to use + a one-time setup. No magic, no daemon.
The server ships a standing capture policy (sent to the client on connect, plus directive tool descriptions), so the agent records things on its own instead of waiting for you to ask:
search_issues for a prior fix.log_issue.append_decision / append_learning.remember_preference, so the one-time correction becomes a pattern it brings back next session.It's proactive but not silent: the agent tells you in one line what it recorded, asks when unsure rather than logging noise, and skips trivia and secrets. You can always override — "log this", or "don't bother". The standing policy is best-effort (it depends on the model following it); for a hard guarantee, add the opt-in Stop hook below.
The standing policy can be forgotten mid-session. The Stop hook makes capture non-optional: when the agent tries to end a turn, it runs once and blocks the stop to ask for one capture pass when either (a) real work happened (file edits or a commit) and nothing was written to project memory, or (b) you corrected how it works and no preference was saved. If memory was already written, or nothing changed and you didn't correct it, the hook stays silent and lets the turn end.
npx -y @kaaustubh/project-memory-mcp install-hook # turn it on (then restart Claude Code)
npx -y @kaaustubh/project-memory-mcp uninstall-hook # turn it off
install does not add it; you enable it explicitly.stop_hook_active), then lets the agent stop.PROJECT_MEMORY_HOOK=off to disable without uninstalling.Capture is only half the loop — the other half is *remembering to look*. The recall hook closes it: every time you submit a prompt, it matches your request against your issue history and decisions/learnings/preferences, and silently injects the strongest hits as context. So a prior fix or decision surfaces without you (or the agent) remembering to search — the "have we hit this before?" habit becomes automatic.
npx -y @kaaustubh/project-memory-mcp install-recall # turn it on (then restart Claude Code)
npx -y @kaaustubh/project-memory-mcp uninstall-recall # turn it off
@xenova/transformers) is installed, recall matches by meaning, so *"the build is broken"* still surfaces an issue logged as *"compile failure"* even앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요