Scaling Karpathy’s LLM wiki: Why your knowledge base needs a graph

Earlier this year, Andrej Karpathy posted his recipe for a personal LLM-maintained knowledge base. Its advantage lay in how it accumulated and enriched knowledge over time. Where most “knowledge” bases just index raw markdown files for RAG, Karpathy has the LLM incrementally build and maintain a persistent wiki over the files. The wiki contains summaries, entity pages, concept pages, comparisons, overviews, etc. This wiki recipe helps solve a key problem in AI. Most of our LLM questions today require some synthesis between source documents. Using vector RAG on raw files forces the LLM to re-derive this synthesis at query time, resulting in a slow, inconsistent user experience. Karpathy’s recipe allows us to persist the synthesized context over time for speed and consistency in personal work — as he initially intended — or potentially extend it to an enterprise knowledge layer. The recipe went viral, and the market noticed. Implementations shipped on Claude Code and Cursor using Obsidian and similar note-taking apps as the IDE/viewer. In June 2026, Google Cloud published a standard representation, a folder of cross-linked markdown it called the Open Knowledge Format (OKF). It’s an emerging standard for portable context. But Karpathy’s approach has clear weaknesses, including persisted…