Your notes.
A living wiki.
Write in Obsidian. Every note connects to a network that keeps growing.
Nothing you read should go to waste.
Lose it, or grow it?
Save an article. Forget where. Search later. Nothing.
Drop it in sources/. Ideas found, pages built, summary ready. In seconds.
Link two notes by hand. One changes. The other stays old. The link dies.
Links stay alive. When new information contradicts the old, every related page stays current.
A year of notes. Just files. Every project starts over. Same reading. Same conclusions.
A year builds a wiki. Every new question draws on everything you've collected. Your knowledge grows.
From note to wiki
One note. A world of connections.
What changes for you
Not features. A new relationship with everything you read.
It just happens
Drop it in sources/. AI reads, extracts, builds. No folders. No tags. No work.
Just talk
Ask like you would a friend. Watch it reason, then answer with sources you can open and trust.
Living links
Every page joins your Obsidian graph. Open Graph View — and watch your knowledge grow.
Stays current
New files? Auto-ingested. Contradictions? Found. Health? Checked. On your terms.
Ready in minutes
Obsidian is all you need.
Install
In Obsidian: Settings → Community plugins → Browse, search Karpathy LLM Wiki, click Install then Enable.
Open Plugin PageConfigure
Settings → Karpathy LLM Wiki. Pick your LLM, enter key, test, save.
Use
Cmd+P (or Ctrl+P). Type Ingest to add sources. Type Query to ask.
Questions, answered
Everything worth knowing, before you start.
What does this plugin actually do?
Drop any note into your Obsidian vault. The AI extracts people, concepts, and theories, then auto-generates interlinked wiki pages with bidirectional links. Ask "what did I write about X?" — the answer comes from your own notes, not the internet.
What are the minimum requirements?
Obsidian v1.11.0+ (desktop: Windows, macOS, Linux) and an LLM provider API key. Supported: DeepSeek, Gemini, Claude, GPT, Kimi, GLM, MiniMax, OpenRouter, or any custom endpoint. No API key needed for local models via Ollama or LM Studio.
Which model should I choose? How much will it cost?
Long-context models recommended — they process your entire wiki in one pass. A single ingest costs $0.05–$0.50. All costs go to the LLM provider; the plugin itself is free and open source.
Can I run it fully offline with local models?
Yes. Install Ollama or LM Studio, pull a model, select it as your provider — no API key, nothing leaves your machine. Cloud providers are better for heavy ingestion (larger context); local models work great for everyday query.
How do I get help or report bugs?
Use GitHub Issues for bug reports, GitHub Discussions for questions. Also find the plugin on the Obsidian Community Plugin page and leave a review.
Every plugin, amplified
Not an island. Every tool you love, now better.
Graph View
See links
Wiki gives your graph meaning. Hub pages, clusters, orphans. Exploration, not decoration.
Web Clipper
Save articles
Drop in sources/. AI extracts, links, updates. One clip becomes 10+ wiki pages.
Dataview
Query data
AI adds structure. Tags, dates, categories. Empty tables become living dashboards.
Git
Track versions
Watch your knowledge evolve. Every commit tells the story of how you grew.
Marp
Make slides
Ask: "Summarize my research for a 10-minute talk." Wiki builds slides from your knowledge.
Canvas
Visual canvas
AI builds concept maps, timelines, decision trees from your knowledge. No more blank canvas.
Your model. Your rules.
Twelve providers, cloud or local. Switch anytime — lock into nothing.
Switch any time
Twelve providers, one dropdown apart. Outgrow one, switch to the next — your wiki never notices.
Privacy first
No backend. No tracking. Nothing stored. Your notes only travel to the AI you choose — or stay fully offline with a local model.
Costs almost nothing
The plugin is free. A full ingest runs you $0.05–$0.50 on the LLM side — pick a value model and you'll barely notice it.
Cloud or local — use the one you trust.
Long-context models read your whole wiki in a single pass — the complete picture, not fragmented snippets.