Context7
Up-to-date docs for AI agents
Descripción del creador (original en inglés)
Read-only access to your Molio local knowledge base: search, read, browse, and AI Q&A.
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
MOLIO_PORTNecesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"molio": {
"command": "npx",
"args": [
"-y",
"@molio/cli",
"mcp"
],
"env": {
"MOLIO_PORT": "your-value"
}
}
}
}Ejecuta esto en tu terminal
claude mcp add molio --env MOLIO_PORT=your-value -- npx -y @molio/cli mcpCompleta los valores vacíos antes de ejecutarlo.
Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"molio": {
"command": "npx",
"args": [
"-y",
"@molio/cli",
"mcp"
],
"env": {
"MOLIO_PORT": "your-value"
}
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"molio": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@molio/cli",
"mcp"
],
"env": {
"MOLIO_PORT": "your-value"
}
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
MOLIO_PORT envEsta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 7 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub zhuzhaoyun, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez –, y la última versión (0.1.2) llegó al registro hace 14 días.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Node.js en tu computadora.
En la misma categoría: Context7, Codebase Memory, Exa
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
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.
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
Give AI your personal knowledge base. Local-first, and every byte stays yours.
English · 中文 · 🌐 Official Website
What you've accumulated isn't one kind of thing: experience and methods scattered across notes, documents, and chat logs — and also whole books, PDFs full of figures, piles of industry material. Most of it sits in a form AI can't read, so every conversation starts from zero.
Molio turns all of it into a personal knowledge base that AI can read and use: Claude Code, Codex, and other agents enter your knowledge space, work on top of everything you've accumulated — researching, writing, answering, analyzing — and write their results back as Markdown, so the base grows thicker with every task. Everything runs on your machine, never through a third-party server.
Starting from zero? The resource library ships ready-made knowledge bases that keep growing — import one and start asking.
▶ Click to watch the demo — one book, one knowledge universe: 1,362 years of history from the *Zizhi Tongjian*, processed by Molio into an AI-explorable knowledge base
01 · Collect & Process — scattered fragments, or a whole book, all become a callable knowledge base
Input isn't limited by format: clip pages with one click via the Web Clipper, open your Obsidian vault directly, or batch-import local documents. Scanned PDFs and Word / PPT / Excel files go through the bundled docling for layout analysis, OCR, and table reconstruction; images are read by OCR; million-word books run through a chunked preprocessing and digest pipeline with resumable progress. What lands on disk is plain Markdown — zero migration, no lock-in.
The Wiki engine then extracts entities and concepts from that unified Markdown layer, weaving dense cross-links and layered indexes — data becomes a foundation only after it's processed.
02 · Work & Task — agents run on your data
Claude Code, Codex, Gemini CLI, Qwen Code — agents work inside your knowledge space, researching, writing, answering, and analyzing. What they see is no blank slate, but everything you've accumulated. Pick an agent in a unified GUI with streaming output; scan a QR code to chat with your base from your phone via WeChat.
03 · Reflow & Deposit — the base grows thicker with use
Every task's output is written back as Markdown — a reusable, long-term asset. The knowledge graph keeps growing, so the next task starts from higher ground. Ready to publish? Typeset with doocs/md and distribute to 30+ platforms in one click.
| Input | What happens |
|---|---|
| Markdown / TXT / HTML / CSV and other text | Read as-is — the default shape of a knowledge base |
| Layout analysis + OCR + table reconstruction → Markdown | |
| Word / PPT / Excel | Same pipeline, heading hierarchy and tables preserved |
| Images (PNG / JPG / TIFF) | OCR text extraction |
| Web pages | One-click clip, or article extraction for WeChat posts |
| Obsidian vault | Opened in place — original files are read-only |
| Million-word books | Chunked preprocessing + digest pipeline, resumable |
PDF and Office conversion is handled by the bundled docling skill. The first conversion downloads the tool plus ~500MB of models; later runs reuse the cache.
Beyond the tool itself, Molio offers ready-made structured knowledge graphs — entire books and professional domains pre-organized into AI-ready knowledge bases you can import in one click, no building from scratch.
Covering literature, history, philosophy, traditional Chinese medicine, medicine, and AI engineering, with new resources added regularly. Highlights include the ontology knowledge base, the *Zizhi Tongjian* knowledge system, and an obstetric-ultrasound knowledge base. See the resource library for the full, always-current catalog.
These are uniquely structured assets that AI cannot conjure on its own. Import one into Molio and instantly run AI Q&A, relationship lookup, and topical research.
[Browse all resources →](https://molio.cn/resources.html)
A single Molio instance can serve multiple channels in parallel. Most channels can be onboarded right from the Web console.
| Channel | Text | Image | File | Voice | Group |
|---|---|---|---|---|---|
| Web Console *(default)* | ✅ | ✅ | ✅ | ✅ | — |
| ✅ | ✅ | ✅ | ⏳ | ⏳ | |
| Feishu | ✅ | ✅ | ✅ | ⏳ | ✅ |
| Telegram | ⏳ | ⏳ | ⏳ | ⏳ | ⏳ |
| Slack | ⏳ | ⏳ | ⏳ | — | ⏳ |
| Discord | ⏳ | ⏳ | ⏳ | — | ⏳ |
✅ Supported · ⏳ Planned · — N/A
Agent Workbench: multi-agent support with streaming responses
Knowledge Space: vault file tree with Markdown rendering
Knowledge Graph: visual map of your knowledge connections
Publishing: one-click sync to 30+ platforms
Download the latest release from GitHub Releases:
Molio-Setup-x.x.x.exeMolio-x.x.x.dmgInstall and launch. On first run, you'll be guided to configure an AI runtime CLI (e.g., Claude Code, Codex, Gemini).
Run Molio as a self-hosted web service on a NAS or server. A single container bundles the daemon, web UI, and Claude Code CLI, and works on both linux/amd64 and linux/arm64 — Synology, QNAP, TerraMaster, TrueNAS, Unraid, and most other NAS devices.
⚠️ How this differs from the desktop app (read before deploying)
- Browser access only — there is no desktop client for this mode. The Electron desktop app only ever talks to the daemon it launches locally (
localhost); it cannot connect to a container running on a remote NAS/server.
- The daemon runs inside the container and can only read/write directories mounted into it. The desktop workflow of "pick any local folder as a knowledge base via a folder picker" does not apply here — the browser has no folder picker, so when you add a knowledge base you type a container-internal path (e.g.
/vaults/your-folder, not the NAS host path).
- To expose folders as knowledge bases, mount them under
/vaultsindocker-compose.ymlvolumes. Your data still stays entirely yours (on your NAS) — only the access model changes, from "desktop app" to "browser + mounted volumes".
One-click install (requires Docker + Docker Compose v2):
# China (recommended)
curl -fsSL https://molio-releases.oss-cn-guangzhou.aliyuncs.com/script/install.sh | bash
curl -fsSL https://raw.githubusercontent.com/zhuzhaoyun/Molio/main/install.sh | bash
bash install.sh
The script asks for your knowledge-base directory and port, then pulls the image and starts the service. When it finishes, open http:// :3100, then go to Settings → Runtimes to configure your AI model and API key. On first boot Molio auto-creates a default knowledge base on the mounted directory, so you land straight inside — no manual setup.
Manual install (if you prefer plain docker compose):
git clone https://github.com/zhuzhaoyun/Molio.git && cd Molio
cp .env.example .env # AI model is configured later in the web UI docker compose up -d # then open http:// :3100
Everyday commands (run inside the install directory, default ~/molio):
docker compose logs -f # follow logs
docker compose restart # restart docker compose pull && docker compose up -d # update to the latest image docke
Solo se muestra el principio; lee el resto en el repo