A Self-Growing Personal Knowledge Base with LLM Wiki, Obsidian, and Agent Tooling
RAG and file-upload chatbots answer questions without accumulating knowledge. An agent-maintained, file-based wiki that preserves source conflicts and builds a cross-linked concept graph turns personal research into a durable, queryable asset that improves with use instead of resetting after every session.
Most AI knowledge tools answer questions by reassembling fragments on the fly, leaving no lasting structure. The LLM Wiki approach, inspired by Andrej Karpathy's public methodology, instead tasks an agent with continuous, incremental maintenance: new material is compared against existing pages, concepts are extracted into their own notes, and conflicting viewpoints are preserved with source and timestamp rather than overwritten. The system splits into a Raw layer for original sources, a Wiki layer for structured understanding, and a Schema layer (`AGENTS.md`) that governs how the agent archives, links, and handles disputes.
Obsidian serves as the local, plain-text storage and visualization layer, while WorkBuddy or Codex acts as the agent execution layer that reads directories, creates pages, and updates bi-directional links. Three build methods are detailed: a direct prompt-driven setup, an open-source `claude-obsidian` plugin that wraps common operations into agent skills, and the WeSight plugin that embeds the entire workflow directly inside Obsidian's interface. The result is a knowledge base that grows with each ingested article or conversation, where the agent handles the repetitive structuring and the human supplies high-value material and editorial judgment.
LLM Wiki inverts the dominant RAG paradigm: instead of retrieving fragments to assemble ephemeral answers, it builds a persistent, incrementally updated knowledge graph that becomes a reasoning substrate for future queries.
Obsidian's plain-text, file-system-native design is not just a preference for local-first purists; it is an architectural enabler that lets an agent operate on knowledge as files, with full Git history and no API lock-in.
The explicit handling of conflicting information—preserving source, time, and scope rather than synthesizing a single summary—addresses a failure mode in AI summarization where nuance is quietly erased.
Packaging the workflow into an Obsidian plugin (WeSight) shifts the interaction model from 'developer prompts an agent in a terminal' to 'knowledge work happens inside the note-taking environment,' lowering the barrier for sustained use.
The system's value compounds: each ingested article enriches the concept and entity graph, so later queries resolve faster and with richer context, making the knowledge base genuinely 'self-growing' rather than just searchable.