LLM Wiki

An autonomous, self-maintaining personal Wikipedia built and maintained by AI.

LLM Wiki transforms your scattered reading and research into a persistent, AI-maintained second brain. Capture documents, notes, and web clippings as you work, and deploy a nightly Claude Routine to autonomously synthesize those sources into a permanent knowledge base. Because the clipper captures your highlights and margin notes alongside the source, the wiki becomes a record of not just what you read but what you thought about it — one that compounds over months and years, long after the original context would have faded. This architecture is heavily inspired by Andrej Karpathy's LLM Wiki concept, with an increased emphasis on autonomous maintenance.

LLM Wiki is designed to work at three distinct scales:

• For you — a personal Wikipedia of what you've read that you don't have to remember to update.
• For your AI — a context layer for LLMs to apply your own mental models when working with you.
• For your organization — most organizations have poor institutional memory, because know-how generally lives in people's heads. We hope companies will consider adopting this model to build a self-maintaining institutional knowledge layer.

Features

• Connect via MCP Connect Claude.ai, Claude Cowork, Claude Code, or Codex (or any other MCP-compatible app)
• A Chrome extension Clip webpages and PDFs as you read, highlight key sections, and leave comments that Claude can see over MCP.
• Uploads Markdown, PowerPoint, PDFs, Word documents, and more.
• A clean Next.js web app to navigate your own wikipedia — and view the underlying sources.
• Native cross-linking between wiki pages, and back to the sources they came from.
• A graph viewer to see how your concepts and entities relate.
• Visualizations — Charts and other visualizations, including SVGs and Mermaid diagrams.

Getting started

LLM Wiki supports two modes: remote & local. You can self-host the remote app, or try it out for free at llmwiki.app. Or you can git clone the repository, and use the CLI to get started.

Here's how to get started locally.

Requirements: Python 3.11+, Node.js 20+. Optional: LibreOffice to extract Word/PowerPoint files, and a MISTRAL_API_KEY for higher-quality PDF OCR.

1. Install. Clone the repo and install the Python and web dependencies.

macOS / Linux:

git clone https://github.com/lucasastorian/llmwiki.git
cd llmwiki
python -m venv .venv && source .venv/bin/activate
pip install -r api/requirements.txt -r mcp/requirements.txt
cd web && npm install && cd ..

Windows (PowerShell):

git clone https://github.com/lucasastorian/llmwiki.git
cd llmwiki
python -m venv .venv
.venv\Scripts\Activate.ps1
pip install -r api/requirements.txt -r mcp/requirements.txt
cd web; npm install; cd ..

If activation is blocked by your execution policy, run Set-ExecutionPolicy -Scope CurrentUser RemoteSigned once, or skip activation and use .venv\Scripts\python in place of python below.

2. Point it at a folder of your files — PDFs, Word documents, PowerPoints, Markdown, notes. Use any folder on your disk (it does not go inside this repo): the one that already holds your documents, or a fresh empty one. LLM Wiki indexes it into a local search index so the files show up in the app and Claude can read them. It never moves, modifies, or uploads your files — the only things it adds are a wiki/ folder for generated pages and a hidden .llmwiki/ index (see What happens on disk).

./llmwiki open ~/research                     # macOS / Linux
python llmwiki open C:\Users\you\research     # Windows

This initializes the workspace, indexes the folder, starts the API and web app, and opens localhost:3000. Local mode is intentionally loopback-only: the API listens on 127.0.0.1 and does not support LAN or remote binding.

To preview the hosted onboarding UX locally without authentication or API writes, start the web app with the development-only preview flag and open localhost:3000/onboarding:

cd web
NEXT_PUBLIC_ONBOARDING_PREVIEW=true npm run dev

Restart the development server after changing this flag. The preview simulates creation and completion in memory; it does not create a wiki or change onboarding state.

3. Connect Claude over MCP. MCP enables Claude to read, write, and search your wiki.

./llmwiki mcp-config ~/research                     # macOS / Linux
python llmwiki mcp-config C:\Users\you\research     # Windows

Paste the printed JSON into claude_desktop_config.json (Claude Desktop) or .claude/settings.json (Claude Code). One workspace is one MCP server entry, so add one per folder. Then tell Claude: "Read the guide, then ingest my sources and start building the wiki."

4. Make it self-maintaining. Set up a Claude Routine — a scheduled prompt that runs on its own — so Claude refreshes the wiki without you having to remember to. Each run, it reads whatever's new in the workspace since last time (uploads, notes, and clips and highlights from the Chrome extension) and updates the pages those sources touch. You curate the sources; the wiki keeps itself current.

A routine prompt that works well:

Read the guide. Find everything added to the workspace since your last run — new sources, clips, and highlights. For each one, read it and update the wiki: write new pages where they're warranted, fold new material into existing pages, and fix any cross-references or citations it affects.

Then schedule that prompt to run nightly. Claude Code Routines run it on Anthropic's cloud on a fixed cadence even when your laptop is closed — create one at [claude.ai/code/routi