WikiBrain

Personal knowledge base. Hosted at wikibrain.app. A hosted implementation of Andrej Karpathy's LLM Wiki pattern: you curate sources, an AI agent compiles them into a persistent, interlinked Markdown wiki, and every AI client you already use (Cursor, Claude, ChatGPT, Claude Code…) reads and writes the same wiki through MCP.

Open-source core (AGPL-3.0). A hosted version with billing, backups and mobile apps is run by the maintainer; self-hosting is fully supported with docker compose.

The idea: Karpathy's LLM Wiki

This project is a direct implementation of the pattern Andrej Karpathy described in his note LLM Wiki (2026). In his words, the LLM "incrementally builds and maintains a persistent wiki" that sits between you and your raw sources, rather than re-reading the sources on every question as RAG does — so knowledge is compiled once and compounds.

WikiBrain keeps his design as-is and only adds hosting and plumbing:

The help page carries a longer summary of the note; the gist itself is the canonical reference.

Related MCP server: Zero Brain MCP Server

What it does

• Three layers. raw/ holds immutable sources, wiki/ holds AI-compiled pages, schema/ holds the compilation rules the agent reads before writing. index.md and log.md follow Karpathy's conventions.

• Ingest, Query, Lint. Sources that no wiki page links to are listed as pending; the agent ingests them (in Cursor via MCP, or on the web with your own API key), answers questions with citations in a chat panel, and health-checks the wiki (orphans, broken links, contradictions) into wiki/lint/.

• Bring sources from anywhere. URLs (readability + metadata, DOI → Crossref, arXiv, PubMed, headless fallback for SPAs), PDF, Word, HTML, Markdown, pasted text, images. BibTeX / CSL-JSON import and export. Zotero sync.

• For researchers. Bibliographic front-matter (doi, authors, year, venue, citation_key), pandoc-style [@citekey] citations rendered as (Author, Year) with an automatic reference list, a literature table view, BibTeX export that works with pandoc.

• Share, script, install. Any page gets a read-only public link (/s/<token>, revocable). The same MCP token works as an API key on the REST API (Authorization: Bearer) for scripts, cron jobs and Claude Code hooks. The web app is an installable PWA with an offline shell.

• MCP server built in. Six tools (get_instructions, search_notes, read_note, create_note, update_note, list_folder) over Streamable HTTP with optimistic locking. Bearer tokens for Cursor; OAuth 2.1 with dynamic client registration for Claude.ai, ChatGPT and other connectors.

• Web UI. Three-pane editor with live preview, backlinks, version history and rollback, force-directed graph with a timeline, front-matter table view (Dataview-style), Mermaid diagrams, Marp slide pages, KaTeX. Traditional Chinese and English interface; write your wiki in any language.

• No lock-in. One-click export of the whole wiki as an Obsidian-compatible Markdown zip, plus .bib / CSL-JSON.

Architecture

One Node service and one PostgreSQL database. Every AI client talks to the same wiki through MCP; the web UI and the server-side agents use the same six tools.

How knowledge moves between the three layers — Karpathy's Ingest / Query / Lint, all done with the same six tools, by your AI client over MCP or by the server-side agent with your key:

The diagrams are generated by scripts/render-diagrams.py (no dependencies); edit the data there and re-run.

Self-host

git clone <this repo> && cd wikibrain
export BETTER_AUTH_SECRET=$(openssl rand -hex 32)
mkdir -p secrets && openssl rand -hex 32 > secrets/wikibrain_key   # encrypts users' API keys; kept out of env vars
export APP_URL=http://localhost:3000        # your public https URL in production (e.g. https://wikibrain.app)
docker compose up -d
open http://localhost:3000

Verification emails are printed to the container log unless RESEND_API_KEY is set. Google sign-in is enabled when GOOGLE_CLIENT_ID/GOOGLE_CLIENT_SECRET are set. The image includes headless Chromium so JavaScript-rendered pages can be imported (about 1.8 GB); set IMPORT_HEADLESS=0 to disable it.

See DEPLOY.md for Replit and other hosts, and .env.example for every variable.

Connect an AI client

• Cursor / Claude Code: Settings → Connect Cursor generates a token and the mcp.json snippet ({ "url": "https://<host>/mcp", "headers": { "Authorization": "Bearer …" } }).

• Claude.ai / ChatGPT / any OAuth-capable client: add a custom connector with the URL https://<host>/mcp, sign in, press Allow. Connections are listed in Settings and can be revoked.

• Web auto-ingest and chat: store your own Anthropic, OpenAI or OpenRouter key in Settings; the server runs the agent with it. Token and cost statistics are shown per job.

Develop

cp .env.example .env         # DATABASE_URL, BETTER_AUTH_SECRET, DEV_MCP_TOKEN
createdb wikibrain           # local PostgreSQL 16
npm install && npm run db:seed
npm run dev                  # API on :3000, web on :5173
npm test                     # backend end-to-end tests (real Postgres)
python3 test/e2e/m3_web.py   # Playwright E2E against the dev servers

Stack: Node 24, TypeScript (strict), Express 5, PostgreSQL (plain SQL migrations), @modelcontextprotocol/sdk, better-auth, React 19, Vite, Tailwind 4. docs/ holds technical notes such as the URL import test matrix and the original UI prototype.

License

AGPL-3.0. You may self-host and modify freely; if you offer a modified version as a network service you must publish your changes. The hosted service's billing, mailing and backup integrations are not part of this repository.