Context7
Up-to-date docs for AI agents
제작자가 쓴 설명(영어 원문)
Verified memory for coding agents: claims cited against code, stale withheld, savings receipts.
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.
필요한 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": {
"kage": {
"command": "npx",
"args": [
"-y",
"@kage-core/kage-graph-mcp"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add kage -- npx -y @kage-core/kage-graph-mcp프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"kage": {
"command": "npx",
"args": [
"-y",
"@kage-core/kage-graph-mcp"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"kage": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@kage-core/kage-graph-mcp"
]
}
}
}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.
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 도메인 kage-core.com 기준으로 확인되었어요(레지스트리가 도메인 소유를 확인해요)
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(2.3.0)은 5개월 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.
State an intent. Kage's orchestrator briefs a coding agent from your repo's own memory, runs it in an isolated git worktree — a single run or a multi-wave goal — and re-runs the checks itself rather than trusting the agent's report:
┌ VERIFIED 3/3 — checks run by Kage, not the agent · build-a-stale-memory-triage-surface-do-n-260818-ec2c
│ "the stale-memory triage surface is built and wired into the review flow" │ ✓ tests ran npm test --prefix mcp → exit 0 evidence/tests.log │ ✓ diff-size inspected at most 800 changed lines evidence/diff-size.log │ ✓ citations inspected every formally cited path exists (directly, or as a unique suffix) in the worktree evidence/citations.log │ · touched 4 file(s), 212 line(s) └────────────────────────────────────────────────────────────────
A real receipt from this repo's own run history. Every row is a command Kage ran or a fact it inspected — never a claim the agent made about itself. kage merge only lands the code once the claim holds, and ratifies what the agent learned, so the next brief, yours or a teammate's, starts smarter.
That memory is the decisions behind your codebase, the runbook for a tricky deploy, the root cause of a gnarly bug — captured as your agents work and checked against the actual code, so what gets reused stays true. It's kept as plain Markdown files in your repo, conformant to the Google Open Knowledge Format (OKF) so there's no lock-in, and shared with your whole team through git. No account, no database, no API key.
npx -y @kage-core/kage-graph-mcp install
Website · Docs · Live viewer · npm · Book a demo
Works with Claude Code · Codex · Cursor · Windsurf · Gemini CLI · Cline · Goose · Roo Code · Kilo Code · OpenCode · Aider · Claude Desktop · Copilot · OpenClaw · Hermes · any MCP client
🌐 English · 简体中文 · 日本語 · 한국어 · Español · Português (Brasil) · Français · Deutsch · हिन्दी
One command, inside your repo, then restart your agent. That's the whole setup.
npx -y @kage-core/kage-graph-mcp install
It creates .agent_memory/, builds the code graph, writes the AGENTS.md / CLAUDE.md policy that tells agents to use Kage, auto-detects and wires your agents, and configures .gitignore + the packet merge driver. Requires Node.js 18+. No account, no API key.
Or just ask your agent to set it up. Paste this into Claude Code, Cursor, or any coding agent:
Set up Kage (verified memory for coding agents, https://github.com/kage-core/Kage)
in this repo: run
npx -y @kage-core/kage-graph-mcp install, then tell me to restart you.
Other ways (plugin · per-agent · memory-only)
# Claude Code / Codex plugin
/plugin marketplace add kage-core/Kage # then: /plugin install kage@kage
kage setup list for all supported)kage setup claude-code --project . --write
kage init --project .
kage setup verify-agent --agent claude-code --project .
</details>
kage room --project . # talk to Kage; it briefs and hires agents for you
kage dispatch " " --agent claude # one delegated run, briefed from repo memory kage runs --project . # what every run is doing right now kage review --project . # read a finished run's claim and diff kage merge --project . # land the code and ratify what it learned
Every run works in its own git worktree. The checks that decide the verdict on the receipt above — tests, diff size, citations — are commands Kage runs itself, never the agent's self-report.
kage app --project starts (or reuses) the local daemon and opens the same room, runs board, and memory view in a UI. From a checkout, npm start --prefix shell runs it as a native window — a thin Electron shell with no HTML of its own, it just loads the daemon's own page — and npm run dmg --prefix shell builds a macOS .dmg (arm64 only; Windows/Linux packaging isn't built yet)."lan": true in .agent_memory/config.json by hand — there's no --lan flag or app toggle yet.kage projects add --agent claude registers another repo the same way the app's "+" button does, then kage app --project opens it.kage app --project <dir>
kage projects add --agent claude
A thin native shell (macOS, arm64 only) over the same daemon the CLI runs — dock presence, a global hotkey, native notifications. Download the latest .dmg from GitHub releases (look for a Kage- .dmg asset).
Unsigned builds show macOS's "unidentified developer" prompt on first launch — right-click the app in Finder and choose Open once. Once installed, it checks for updates on launch and every 4 hours and installs on restart; ad-hoc (unsigned) builds can't self-install and notify you instead, linking back to the releases page.
Prefer the CLI? The one-line install works everywhere the app doesn't need to:
npx -y @kage-core/kage-graph-mcp install
Kage is an orchestrator for coding agents, built on a memory layer. As your agent works, it captures what it learns (decisions, bug fixes, conventions, how the code fits together) as Open Knowledge Format (OKF) concept files committed in your repo under .agent_memory/. The next session (yours or a teammate's) starts already knowing it, instead of re-reading or re-asking.
Three things make it different from other memory tools:
From day one, Kage kept agent memory as plain files in your repo — no cloud, no database, no lock-in, while everyone else was building memory clouds. In June 2026, Google Cloud shipped the Open Knowledge Format: knowledge as Markdown in git, vendor-neutral, no account — the exact thesis Kage already ran on. So Kage adopted OKF as its standard, and supercharges it with the layer OKF deliberately leaves out:
The trust metadata rides in OKF-legal x-kage-* fields, so a Kage bundle stays 100% conformant and opens in any OKF consumer, including Google's own visualizer. OKF standardizes the store; Kage is the verification and freshness layer Google left out.
Once installed, it's ambient. You don't run anything by hand:
kage pr check) and withheld from recall until it's re-verified or replaced, so knowledge can't quietly rot.Watch it happen in the local dashboard (kage viewer): packets, the memory↔code graph, trust gates, and live events stream in as the agent works. Wrap anything in … and it's never stored.
kage viewer: the memory engine underneath the orchestrator above — your team's decisions, runbooks, and bug fixes (purple), kept in the repo and linked to the code they are about (blue).
Most memory tools (claude-mem, agentmemory, mem0, Zep) store memory per-machine or in a cloud you don't own, and never re-check it against the code. Kage keeps it in your repo and verifies it, so it stays your team's and stays true as the code changes.
| Kage | claude-mem | mem0 / Zep | |
|---|---|---|---|
| Automatic capture + session-start recall | ✓ | ✓ | via SDK |
| Hallucinated citations rejected at write time | ✓ | — | — |
| Stale memory withheld at recall (cited files deleted/changed, TTL, reported) | ✓ | — | — |
| Diff-time stale-catch, warned before the PR when your change breaks a memory | ✓ | — | — |
| Memory reviewed in git, same PR as the code (plain files, no DB) | ✓ | SQLite + cloud | hosted API |
Codify memory into team SKILL.md files agents auto-load | ✓ (kage skills) | — | — |
| Cross-mach
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