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

Karpathy’s LLM-maintained wiki concept aims to preserve synthesized knowledge from source documents rather than repeatedly deriving it through raw-file vector RAG, but the approach can suffer from context growth, duplicate content, stale summaries, and inefficient navigation in file-based Markdown collections. The post argues that representing Markdown files, sections, reading order, and links in a Neo4j graph enables scalable progressive disclosure, allowing agents to traverse relationships, inspect backlinks, find paths, rank central documents, and identify communities without scanning an entire corpus or relying solely on similarity search. It presents ki as an open-source, deterministic tool that indexes Markdown into a disposable Neo4j graph without modifying source files or requiring LLM or embedding work during ingestion, offering commands for outlines, full-text search, ordered content retrieval, and direct Cypher queries. Citing research from Newcastle University’s National Innovation Centre for Data, the post says agents combining graph navigation with vector search outperformed vector-only agents on complex factual questions, while also describing a planned ki theme-detection feature based on graph community detection. It concludes that graph-based retrieval could support personal knowledge bases, shared team documentation, agent memory, and structured enterprise knowledge layers by making explicit document structure and links navigable as collections grow.

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