Permet à un client IA de rechercher, lire et enrichir un wiki Markdown lié, avec des réponses citées provenant des documents ingérés.
Aussi connu sous openapi/gknowledge, gknowledge-mcp
- Étoiles
- 29
- Outils
- —
- Téléchargements, 30 jours
- —
- Dernière version
- 0.3.0
- Dernier commit
- 27 oct. 2026
- Licence
- MIT
Qu'est-ce que gknowledge MCP ?
gknowledge est un serveur MCP. Permet à un client IA de rechercher, lire et enrichir un wiki Markdown lié, avec des réponses citées provenant des documents ingérés.
Résumé généré à partir du README du projet
À propos du MCP server gknowledge
La création d'une base de connaissances persistante et évolutive à partir de vos documents est ce que gknowledge permet aux chercheurs et aux développeurs. Il implémente le modèle LLM Wiki en ingérant des fichiers sources pour créer un wiki Markdown lié qu'une IA peut maintenir au fil du temps. Le système utilise un processus d'extraction en deux étapes pour garantir que chaque page générée inclut sa provenance et ses citations, rendant l'information vérifiable et fiable.
Les utilisateurs peuvent exécuter le système entièrement hors ligne en utilisant un moteur heuristique intégré, ou le connecter à un modèle de prédilection tel qu'Ollama, LM Studio, vLLM, ou un forfait ChatGPT via Sign in with ChatGPT. Il est conçu pour une confidentialité axée sur le local, en stockant tout en Markdown brut avec du frontmatter YAML et un index SQLite, ce qui rend le coffre compatible avec Obsidian. Le MCP server permet à des agents comme Claude Code, Codex ou Cursor d'interagir directement avec votre base de connaissances.
Via la commande gknowledge-mcp, un client IA peut effectuer des tâches spécifiques comme la recherche de mots-clés, la lecture de pages avec leurs backlinks, l'énumération des pages par type, ou la pose de questions recevant des réponses citées. Vous pouvez également utiliser le serveur pour ajouter de nouvelles sources à ingérer ou pour surveiller l'activité récente au sein du wiki.
Ce que vous pouvez faire avec
- 1Croissance automatisée du wikiUn agent IA peut ingérer de nouveaux articles de recherche pour mettre à jour et étendre automatiquement votre base de connaissances Markdown existante.
- 2Gestion de wiki agentiqueLes développeurs peuvent connecter des assistants de code à leurs coffres de connaissances locaux pour rechercher et lire des pages de wiki spécifiques.
Extrait du README du projet. Chaque cas d'utilisation est vérifié par rapport à une citation du document.
Comment installer gknowledge MCP
Formats vérifiés le 10 nov. 2026Installer gknowledge MCP dans Claude Code
$ claude mcp add gknowledge -- uvx gknowledge-mcpInstaller gknowledge MCP dans Cursor
~/.cursor/mcp.json
{
"mcpServers": {
"gknowledge": {
"command": "uvx",
"args": [
"gknowledge-mcp"
]
}
}
}Installer gknowledge MCP dans VS Code
.vscode/mcp.json
{
"servers": {
"gknowledge": {
"type": "stdio",
"command": "uvx",
"args": [
"gknowledge-mcp"
]
}
}
}Installer gknowledge MCP dans Claude Desktop
claude_desktop_config.json
{
"mcpServers": {
"gknowledge": {
"command": "uvx",
"args": [
"gknowledge-mcp"
]
}
}
}Installer gknowledge MCP dans Codex
~/.codex/config.toml
[mcp_servers.gknowledge] command = "uvx" args = ["gknowledge-mcp"]
Installer gknowledge MCP dans Gemini CLI
~/.gemini/settings.json
{
"mcpServers": {
"gknowledge": {
"command": "uvx",
"args": [
"gknowledge-mcp"
]
}
}
}Vérifications automatisées
Exécuté le 13 nov. 2026Rapport complet →A passé nos vérifications automatisées. Il ne s'agit pas d'un audit de sécurité : lisez le code et donnez au serveur l'accès le plus restreint dont il a besoin. Fonctionnement des vérifications
- Dépôt source lié et accessibleRéussi
- Licence open source (OSI)Réussi
- Commit ou version publiée au cours de l'année écouléeRéussi
- Nom du package non proche d'un package populaireRéussi
- L'endpoint distant utilise HTTPSNon vérifié
- Les outils destructifs sont étiquetésNon vérifié
- Aucune instruction cachée destinée au modèleRéussi
- Aucun avis critique dans le paquet ni ses dépendancesRéussi
README
Voir sur GitHubInstall
One line, on Ubuntu 24.04+ and Debian 13+. It adds the signed gknowledge APT repository, so updates arrive with apt upgrade (other distributions get a per-user install):
curl -fsSL https://github.com/openapi/gknowledge/releases/latest/download/install.sh | bash
Or add the APT repository yourself. apt also pulls GTK 4, Libadwaita and PyGObject:
sudo curl -fsSLo /usr/share/keyrings/gknowledge.gpg https://openapi.github.io/gknowledge/gknowledge.gpg
sudo curl -fsSLo /etc/apt/sources.list.d/gknowledge.sources https://openapi.github.io/gknowledge/gknowledge.sources
sudo apt update && sudo apt install gknowledge
On any distribution, with Flatpak (the GNOME runtime comes from Flathub):
flatpak install --user https://openapi.github.io/gknowledge/io.github.openapi.Gknowledge.flatpakref
Both repositories are described at openapi.github.io/gknowledge.
Then open gknowledge from the app grid. To remove it: sudo apt remove gknowledge (or flatpak uninstall io.github.openapi.Gknowledge).
- Fedora, Arch and others: install PyGObject, GTK 4 and Libadwaita with your package manager, then run the one-line installer above. It installs into
~/.localwithout root. - From source:
git clone https://github.com/openapi/gknowledge && cd gknowledge && make install. - Single files: every version's
.deband Flatpak bundle is on the Releases page, e.g.sudo apt install ./gknowledge_all.debafter downloading gknowledge_all.deb.
Why gknowledge
If you have used LLM Wiki, Tolaria, OpenKnowledge, Reor or Khoj, you already know the loop: ingest sources, let the model build and maintain a wiki, then ask questions against it. gknowledge does the same thing as a real GNOME application.
- Native, not a web view. GTK 4 and Libadwaita, built to follow the GNOME HIG. No Electron, no Tauri, no browser engine.
- Works with no account. A built-in heuristic engine ingests and answers offline. Add a model when you want better synthesis.
- Your data stays yours. Markdown with YAML frontmatter plus a readable SQLite index. Open the folder in Obsidian or any editor.
- Cited, reviewable, incremental. Every generated page records its sources, answers cite pages, and a review queue keeps you in control.
The workflow you already know
| You know this from… | What it is | In gknowledge |
|---|---|---|
| Karpathy's LLM Wiki, LLM Wiki | Ingest: read a new source, update the pages it touches | Done: two-step extraction with provenance |
| Reor, Khoj | Ask: questions answered from your notes, with citations | Done: lexical search plus graph expansion, citations, saved answers |
| LLM Wiki, OpenKnowledge | index.md and log.md, [[wikilinks]], Obsidian-compatible vault |
Done |
| LLM Wiki, Tolaria | Review AI changes before they stick | Review queue done, Git diff planned |
| Karpathy's LLM Wiki, OpenKnowledge | Lint: orphans, contradictions, stale pages | Planned |
| LLM Wiki | Knowledge graph, clusters, gaps | Planned |
| Tolaria, OpenKnowledge, LLM Wiki | MCP server so agents can read and write the wiki | Done: gknowledge-mcp |
| LLM Wiki | Web clipper, folder watch, deep research | Planned |
See ROADMAP.md for dates and TODO.md for the backlog.
Karpathy's LLM Wiki, native on GNOME
In April 2026 Andrej Karpathy described the LLM Wiki pattern: instead of re-reading raw documents on every question, an LLM builds and maintains a persistent wiki that compounds over time. gknowledge implements it as a desktop app.
What we kept
- Three layers: immutable
sources/, the generatedknowledge/wiki, and settings that tell the engine how to work. - The operations: ingest (read a source, update the pages it touches) and query (answer from the wiki, with citations). Lint is next.
index.mdas the catalogue andlog.mdas the append-only history.- Plain Markdown with
[[wikilinks]]and YAML frontmatter, so the folder opens in Obsidian. - The human curates, the model maintains.
What we changed and added
- A native app instead of an idea file. GTK 4 and Libadwaita, with sources, wiki, assistant and review in one window.
- Works with no model at all. An offline engine ingests and answers; a model makes it better.
- Bring your own brain. Your ChatGPT plan through Sign in with ChatGPT, Ollama, or any OpenAI-compatible endpoint.
- Two-step ingest with provenance. Analysis first, then pages; every page records its sources.
- No empty pages, no duplicates. Pages are created only with real content, names are merged across sources ("Kasparov" and "Garry Kasparov"), minor names stay as mentions until a second source cites them.
- A review queue for what the model is unsure about.
- An MCP server, so Claude Code, Codex or Cursor can search, read and grow the same wiki.
Use your wiki from Claude Code, Codex or Cursor
gknowledge ships an MCP server. Point any agent at a project folder:
claude mcp add gknowledge -- gknowledge-mcp ~/Knowledge/my-project # Claude Code
codex mcp add gknowledge -- gknowledge-mcp ~/Knowledge/my-project # Codex CLI
For Cursor or other clients, add this to the MCP configuration:
{ "mcpServers": { "gknowledge": { "command": "gknowledge-mcp", "args": ["/home/you/Knowledge/my-project"] } } }
| Tool | What the agent can do |
|---|---|
search_wiki |
find pages by keyword |
read_page |
read a page with its sources and backlinks |
list_pages |
list pages by type |
ask |
get a cited answer, optionally saved back into the wiki |
add_source |
import and ingest a file |
recent_activity |
see what changed recently |
Try it from source
sudo apt install python3-gi gir1.2-gtk-4.0 gir1.2-adw-1 # Ubuntu / Debian
git clone https://github.com/openapi/gknowledge && cd gknowledge
make start # opens a demo project with six AI papers already ingested
make start also restarts the app whenever you edit the source, which makes it the fastest way to hack on gknowledge. Install it for daily use with make install.
Bring your own brain
| Provider | Needs | Notes |
|---|---|---|
| Offline (default) | nothing | Heuristic extraction and extractive answers. Fully private. |
| ChatGPT plan | A Plus or Pro account | Preferences, Provider, Sign in with ChatGPT. Usage is charged to your own plan, no API key. |
| Ollama / LM Studio / vLLM | A local server | Any OpenAI-compatible endpoint on your machine. |
| OpenAI-compatible cloud | An API key | OpenAI, OpenRouter, Together, and similar. |
Nothing is sent anywhere unless you pick a provider that is not offline.
How it compares
| gknowledge | LLM Wiki | Tolaria | OpenKnowledge | Reor (archived) | Khoj | |
|---|---|---|---|---|---|---|
| Native GNOME / GTK 4 | yes | Tauri | Tauri | Electron | Electron | web + Electron |
| Works offline, no key | yes | needs a model | AI optional | agent-driven | local model | self-host |
| Plain Markdown vault | yes | yes | yes | yes | yes | reads yours |
| Linux packaging | .deb, Flatpak |
.deb, AppImage |
AppImage, .deb |
.deb, .rpm |
AppImage | pip, Docker |
| License | MIT | GPL-3.0 | AGPL-3.0 | GPL-3.0 | AGPL-3.0 | AGPL-3.0 |
Details and history of each project are in DISTRIBUTING.md.
gknowledge is an alternative to…
gknowledge sits at the intersection of LLM-powered knowledge bases, local-first note tools, and document RAG systems. If you have used any of the tools below, you already understand the shape of the problem — gknowledge addresses it as a native GNOME/GTK application that is local-first, provider-agnostic, and usable fully offline (no API key required, thanks to a built-in heuristic extraction engine).
LLM wiki / auto-knowledge-base builders (the closest peers, several studied in NOTES.md):
- LLM Wiki (
nashsu/llm_wiki) — Tauri desktop app implementing Karpathy's incremental "LLM builds and maintains a wiki" pattern - Tolaria — files-first, git-first Markdown knowledge base with AI-assisted editing
- WeKnora (Tencent) — enterprise RAG + agents + auto-wiki framework
- OpenKnowledge (Inkeep) — WYSIWYG Markdown editor with agent/MCP integrations for LLM wikis
- Reor — local-first AI note-taking that auto-links related notes
- Khoj — self-hostable AI "second brain" over your documents
Not a peer: Google NotebookLM offers a similar cited-notebook experience but is closed-source and cloud-only.
Document RAG / "chat with your knowledge" engines:
Local-first / networked-thought note tools (which gknowledge complements with automated ingestion and provenance):
- Obsidian (+ Smart Connections / Copilot plugins), Logseq, SiYuan, Trilium Notes, Zettlr, Foam, Dendron, Joplin, Notion, Tana, Capacities, Heptabase, RemNote
Document / reference managers with growing AI features:
How gknowledge differs
- Native to GNOME, not Electron/Tauri or web-first — GTK 4 + Libadwaita, HIG-aligned, Flatpak-friendly.
- Offline-first and provider-agnostic — a local heuristic engine ships in the box; OpenAI-compatible endpoints (OpenAI, Ollama, LM Studio, vLLM, OpenRouter, …) are optional, swappable, and configured per project.
- Inspectable, user-owned format — knowledge is plain Markdown with YAML frontmatter (Obsidian-compatible) plus a readable SQLite index; nothing is locked behind a sync service.
- Incremental, not stateless RAG — sources are ingested once into a persistent, cross-linked knowledge space with provenance, rather than re-retrieved from scratch on every question.
- Human-in-the-loop — generated content is clearly marked and a review queue keeps you in editorial control.
Project docs
ROADMAP.md— milestones and the features we are borrowing from peersTODO.md— actionable backlogDISTRIBUTING.md— positioning and distribution study of comparable projects (Italian)CONTRIBUTING.md— how to contributedocs/DESIGN.md— the original product and design documentCHANGELOG.md— what changed in each release
License
MIT. Contributions are welcome, see CONTRIBUTING.md.
Star history
If gknowledge is useful to you, a star helps other GNOME users find it.
Questions sur gknowledge MCP
Comment ajouter gknowledge MCP à Claude Code ?
Exécutez claude mcp add gknowledge -- uvx gknowledge-mcp dans votre terminal. Ajoutez --scope user pour le rendre disponible dans tous les projets.
Comment ajouter gknowledge MCP à Cursor ?
Ouvrez ~/.cursor/mcp.json et ajoutez le serveur sous mcpServers, avec la configuration Cursor indiquée dans la section d'installation ci-dessus.
Une clé API est-elle requise pour utiliser gknowledge ?
Non, il fonctionne par défaut hors ligne grâce à un moteur heuristique intégré, bien que vous puissiez vous connecter à des endpoints compatibles OpenAI si vous le souhaitez.
Le wiki est-il compatible avec Obsidian ?
Oui, il utilise du Markdown brut avec des wikilinks et du frontmatter YAML, ce qui rend le dossier compatible avec Obsidian.