原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
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llmwiki

作者:atomicstrata版本 1.5.0i★ –i在你的电脑上运行
注册名称io.github.atomicstrata/llmwiki

安装

概览

作者提供的说明(英文原文)

Compile documents into a cited knowledge wiki. Retrieve evidence, query, and review via local MCP.

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。

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

  • npm已安装 Node.js(用于 npx 命令)

你用的是哪个应用?

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": {
    "llmwiki": {
      "command": "npx",
      "args": [
        "-y",
        "llm-wiki-compiler",
        "serve",
        "--root",
        "your-value"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add llmwiki -- npx -y llm-wiki-compiler serve --root your-value

这个服务器需要在启动命令中填入值,请复制设置后自行填写。

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "llmwiki": {
      "command": "npx",
      "args": [
        "-y",
        "llm-wiki-compiler",
        "serve",
        "--root",
        "your-value"
      ]
    }
  }
}

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。

这个服务器需要在启动命令中填入值,请复制设置后自行填写。

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "llmwiki": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "llm-wiki-compiler",
        "serve",
        "--root",
        "your-value"
      ]
    }
  }
}

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 atomicstrata 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 7248 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 7248 个服务器的 68% 更新
  • 基础检查结果等于或优于此分类中其他 7248 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

llmwiki 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(1.5.0)于18天前进入注册中心。

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 Node.js。

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★ 64,224MIT今天更新
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★ 5,213MIT上个月更新
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。

llmwiki

New in 1.5 — Wiki behavior separated from storage.

1.5.0 is the stable release on npm latest: npm install --global llm-wiki-compiler.

  • Same wiki, cleaner foundation. Compilation, retrieval and review now describe what they need from storage instead of touching files directly. Your existing wiki keeps its layout and works as before, with no migration.
  • Converse gateways. Use a bearer-authenticated Converse endpoint as your provider.
  • Pages that fit the source. llmwiki compile --no-pages-limit drops the fixed concept count.
  • A viewer that keeps up. llmwiki view refreshes after a compile without a restart and reads its port from LLMWIKI_VIEW_PORT.

What's in 1.5.0 · Changelog

New in 1.4 — Review answers before publishing.

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

New in 1.3 — A fresh look for your wiki.

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.

New in 1.2 — Explore your records and their evidence.

Browse profile-defined categories, declared fields, connected records, provenance, and supporting source passages in the local viewer.

New in 1.1 — Publish and share domain templates.

Create signed template distributions, discover them through explicitly trusted catalogs, and install or update them with compatibility checks.

New in 1.0 — Configurable Lifecycle Profiles.

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 →


What llmwiki does

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.

When to use this repo

Use llmwiki when you need a persistent knowledge base from raw material:

  • Compile papers, notes, READMEs, transcripts, PDFs, images, or web pages into typed wiki pages.
  • Give agents a stable, citation-aware context pack instead of a pile of loose files.
  • Keep generated knowledge auditable with source citations, review queues, freshness checks, and quality gates.
  • Browse the result locally, query it from the CLI, expose it over MCP, or embed it through the SDK.
  • Exchange compiled knowledge with other tools using Open Knowledge Format (OKF), JSON, JSON-LD, GraphML, Marp, and 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.

What you get

  • Compiled wiki, not chunks. A two-phase LLM pipeline extracts concepts, then generates typed pages: concept, entity, comparison, and overview.
  • Configurable Lifecycle Profiles. A fail-closed .llmwiki/profile.json can declare entity schemas, typed relations, lifecycle state machines, transition requirements, workflows, artifacts, connectors, content tiers, and retrieval policy.
  • Installable domain templates. 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.
  • Runtime trust gates. Relation, evidence, artifact, and human/agent gates are enforced by the write path rather than left as prompt conventions; standing lint detects drift after the fact.
  • Citation-traceable output. Paragraphs and claims cite source files and line ranges, and llmwiki lint validates the links.
  • Hybrid retrieval. Semantic chunk search, BM25 reranking, and wikilink graph expansion build compact evidence packs for queries and agents.
  • Local viewer. llmwiki view opens a read-only browser UI with search, page metadata, graph exploration, source-freshness badges, and citation chips.
  • Review policy. Generated pages can be auto-held for review when confidence, contradiction, schema, or provenance rules trip.
  • Freshness repair. llmwiki lint and llmwiki next surface stale/orphaned pages; llmwiki refresh --stale repairs changed knowledge without compiling unrelated new sources.
  • Eval harness. 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.
  • MCP server. llmwiki serve exposes ingest, compile, query, lint, read, status, eval, context-pack, and OKF exchange tools to MCP-compatible agents.
  • SDK. createWiki({ root }) drives ingest, compile, query, context, status, export, eval, and OKF import/export from TypeScript without shelling out.
  • Open Knowledge Format exchange. Export and import OKF bundles for portable, markdown-native knowledge exchange. External OKF imports are staged through the review queue by default; trusted bundles can be written live explicitly.
  • Other portable exports. Export JSON, JSON-LD, GraphML, Marp slides, and llms.txt for downstream systems.
  • Provider portable. Anthropic, Claude Agent SDK local login, OpenAI Codex CLI local login, OpenAI-compatible servers, Ollama, GitHub Copilot, Atlas Cloud, OrcaRouter, Converse-compatible gateways, and local OpenAI-compatible runtimes.

Configurable Lifecycle Profiles (CLP)

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:

  • typed entities, fields, and directed relations;
  • lifecycle states, transition evidence, and trust gates;
  • multi-stage workflows and declared actions;
  • hash-pinned artifacts and first-party connector bindings; and
  • content tiers and retrieval behavior.

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

or install a built-in or local declarative template

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.

Karpathy's LLM Wiki pattern

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.

Agent decision guide

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:

GoalUse
Create a wiki from one source and inspect itllmwiki quickstart
Start a typed research or editorial projectllmwiki template list, then `llmwiki template init autosci\newsroom`
Inspect or validate the active domain contractllmwiki profile show and llmwiki profile validate

| Run a declared lifecycle workflow | llmwiki workflow list, then `llmwiki workf

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