Context7
Up-to-date docs for AI agents
제작자가 쓴 설명(영어 원문)
Compile documents into a cited knowledge wiki. Retrieve evidence, query, and review via local MCP.
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": {
"llmwiki": {
"command": "npx",
"args": [
"-y",
"llm-wiki-compiler",
"serve",
"--root",
"your-value"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add llmwiki -- npx -y llm-wiki-compiler serve --root your-value이 서버는 실행 명령어에 값을 넣어야 해서, 설정을 복사한 뒤 직접 채워 주세요.
프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"llmwiki": {
"command": "npx",
"args": [
"-y",
"llm-wiki-compiler",
"serve",
"--root",
"your-value"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 실행 명령어에 값을 넣어야 해서, 설정을 복사한 뒤 직접 채워 주세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"llmwiki": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"llm-wiki-compiler",
"serve",
"--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.
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 6일 점검
레지스트리 이름이 GitHub 계정 atomicstrata 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(1.5.0)은 17일 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.
1.5.0 is the stable release on npm latest: npm install --global llm-wiki-compiler.
llmwiki compile --no-pages-limit drops the fixed concept count.llmwiki view refreshes after a compile without a restart and reads its port from LLMWIKI_VIEW_PORT.What's in 1.5.0 · Changelog
Review generated answers and their citations before they become pages, approve a batch of pages in one operation, and trace an answer back to the pages that informed it. Release notes · Upgrade guide
Meet Scientific Clay, with soft surfaces and rounded typography, and Minimal, which follows your system’s light or dark setting. Switch instantly between four themes, including Nebula Light and Dark.
Scientific Clay Minimal
The same page in a demonstration wiki. Click either screenshot for a closer look.
Recursive source folders, path exclusions, project-specific compile instructions, and storage for larger embedding indexes.
Browse profile-defined categories, declared fields, connected records, provenance, and supporting source passages in the local viewer.
Create signed template distributions, discover them through explicitly trusted catalogs, and install or update them with compatibility checks.
Build a knowledge system around the way you work. Define your records, relationships, review gates, and workflows in one validated profile. Start with AutoSci for research or Newsroom for editorial work, or create your own.
Explore Configurable Lifecycle Profiles →
Compile raw sources into an interlinked, citation-traceable markdown wiki that agents and humans can browse, query, lint, export, and reuse. The default profile preserves the classic concepts-and-queries layout; optional profiles add domain-specific types and workflows without adding domain branches to the compiler.
llmwiki implements the LLM Wiki pattern: instead of re-discovering knowledge from raw files at query time, compile it once into durable pages that accumulate structure, provenance, review state, and retrieval metadata over time.
Use llmwiki when you need a persistent knowledge base from raw material:
llms.txt.Do not use llmwiki as a general static-site generator, a heavy ontology database, or a replacement for ad-hoc search over fast-changing raw logs. It is strongest when source knowledge is worth compiling, reviewing, and reusing.
concept, entity, comparison, and overview..llmwiki/profile.json can declare entity schemas, typed relations, lifecycle state machines, transition requirements, workflows, artifacts, connectors, content tiers, and retrieval policy.llmwiki template init autosci creates a research project with papers, ideas, experiments, manuscripts, evidence artifacts, workflows, and Crossref import. newsroom demonstrates the same machinery for editorial work.llmwiki lint validates the links.llmwiki view opens a read-only browser UI with search, page metadata, graph exploration, source-freshness badges, and citation chips.llmwiki lint and llmwiki next surface stale/orphaned pages; llmwiki refresh --stale repairs changed knowledge without compiling unrelated new sources.llmwiki eval reports health score, a per-page health distribution that flags the worst pages, wikilink-graph health, citation coverage/precision, corpus stats, regression deltas, and optional judge-model citation support.llmwiki serve exposes ingest, compile, query, lint, read, status, eval, context-pack, and OKF exchange tools to MCP-compatible agents.createWiki({ root }) drives ingest, compile, query, context, status, export, eval, and OKF import/export from TypeScript without shelling out.llms.txt for downstream systems.CLP turns llmwiki's knowledge compiler into a reusable substrate for domain-specific knowledge systems. A validated .llmwiki/profile.json is the single contract for:
These rules are enforced by the runtime, not left as prompt conventions. The CLI, SDK, MCP server, viewer, context builder, lint, status, export, and OKF exchange surfaces all operate from the same profile contract. Invalid profiles and writes that bypass a declared gate fail closed.
CLP is backward-compatible by construction: a project without .llmwiki/profile.json uses the built-in default concepts-and-queries profile and preserves the pre-1.0 behavior. You can start three ways — scaffold your own profile, install a built-in or local template, or install a signed template from a trusted tap:
# author your own profile, one entity type at a time
llmwiki profile init research --entity paper
llmwiki template list llmwiki template inspect autosci llmwiki template init autosci
llmwiki profile validate llmwiki workflow list
autosci is a practical research system with papers, ideas, experiments, manuscripts, evidence artifacts, workflows, and Crossref ingestion. newsroom applies the same generic machinery to articles, desks, bylines, and editorial workflows. Templates contain configuration and examples, never executable plugin code.
Templates can also be distributed securely. Publishers build signed, offline distributions with llmwiki template publish — Ed25519 signing, key rotation, and package revocation — and verify them with template publish verify. Consumers add explicitly trusted taps, discover and inspect signed catalogs, and install or update templates with continuity, revocation, and compatibility checks enforced under lock.
Read the CLP concept guide, follow the AutoSci research workflow, or explore the Newsroom editorial workflow.
Andrej Karpathy described the LLM Wiki pattern as a way to turn raw material into compiled knowledge that future agents can reuse. llmwiki is a concrete compiler for that pattern.
The key shift is moving work from query time to compile time. Traditional RAG repeatedly retrieves raw chunks and asks the model to reconstruct relationships for each question. llmwiki first turns sources into typed, interlinked pages with citations, metadata, and review state. Queries, context packs, exports, and MCP tools then operate over that compiled artifact.
That makes llmwiki useful when knowledge should compound: concepts shared across sources become one page, saved answers become future context, stale pages can be detected and repaired, and agents can consume a stable evidence pack instead of re-reading the same raw files from scratch.
See docs/concepts/karpathy-pattern.mdx for the deeper explanation.
Use llmwiki for a reusable corpus of documents, research, or project knowledge. Start with wiki_status and get_context_pack when you need evidence for your own reasoning. A one-off file read or live web search does not need a wiki.
The npm package includes an Agent Skill. See the MCP setup guide for installation. The MCP Registry identifier is io.github.atomicstrata/llmwiki; the npm package is llm-wiki-compiler and the executable is llmwiki.
Choose the entry point that matches the task:
| Goal | Use | |
|---|---|---|
| Create a wiki from one source and inspect it | llmwiki quickstart | |
| Start a typed research or editorial project | llmwiki template list, then `llmwiki template init autosci\ | newsroom` |
| Inspect or validate the active domain contract | llmwiki profile show and llmwiki profile validate |
| Run a declared lifecycle workflow | llmwiki workflow list, then `llmwiki workf
앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요