Karpathy-style LLM Wikis for the Hermes Agent — persistent, compounding knowledge bases curated by AI agents

Hermes Wiki

Hermes Wiki implements Karpathy’s LLM Wiki pattern as a production-ready plugin for the Hermes Agent. Instead of rediscovering knowledge on every query, agents build and maintain persistent wikis that compound knowledge over time.

Why LLM Wikis?

Traditional RAG uploads documents and retrieves chunks at query time. The LLM never accumulates understanding. Karpathy’s insight: LLMs should curate, not just retrieve.

With Hermes Wiki:

   
Getting Started Install, create your first wiki, ingest a source
CLI Reference Full command documentation
Agent Tools Using wikis from Hermes agent conversations
Architecture Storage, pipeline, adapters, privacy
Hooks Architecture Per-wiki executable customization design
Dashboard Web UI setup and views
Karpathy Pattern How Hermes Wiki implements the LLM Wiki concept
Quality Audit Audit findings and roadmap for evals, features, and test suites
Media Ingestion Design Decision record and build plan for multimodal ingestion

The Pattern

Raw Sources (immutable)     →  Ingest Pipeline  →  Wiki Pages (agent-curated)
articles, papers, transcripts   classify/process     entities, concepts, comparisons
                                                     cross-linked, attributed, versioned

Markdown is authoritative. SQLite is a rebuildable projection. Git tracks everything.