llm-wiki

To build a persistent markdown knowledge base, you need a tool that ingests research sources, summarizes content, and cross-references new notes with existing files to prevent knowledge fragmentation. This maintains long-term retention by automatically linking concepts and flagging contradictions.

A self-compounding knowledge base automatically cross-references new information with existing markdown notes to deepen your domain model. It works by having an agent ingest sources, summarize data, lint files, and maintain a structured index, ensuring your research evolves coherently.

Unlike RAG alternatives that retrieve from static documents, a markdown knowledge base compounds knowledge by actively interlinking notes and flagging contradictions. This approach ensures data integrity through schema adherence and agent-led maintenance, providing durable, queryable research retention.

Yes, maintaining local markdown notes for complex research requires consistent adherence to a defined schema and taxonomy. This strict structural requirement ensures data integrity, prevents information sprawl, and allows the agent to automatically cross-reference and lint files effectively.

The primary limitation of agent-led knowledge base maintenance is the strict dependency on a defined schema and taxonomy to prevent information sprawl. Without consistent structural adherence, the agent cannot effectively cross-reference sources or maintain data integrity across local file systems.