Kip

Bridges the gap between LLM probabilistic reasoning and deterministic knowledge graphs through a graph-oriented query language. Exposes KQL for queries (FIND, WHERE, FILTER), KML for knowledge manipulation (UPSERT, DELETE), and META instructions for schema discovery. Think of it as giving your LLM a persistent memory layer where conversations and observations become structured "Knowledge Capsules" stored in a Cognitive Nexus graph. The protocol returns JSON and uses Prolog-style syntax optimized for Transformer models. Reach for this when you need explainable AI interactions, long-term memory across sessions, or want to eliminate hallucinations by grounding responses in a queryable knowledge base. Includes pre-built capsules for bootstrapping and a Hippocampus layer that lets business agents use natural language without knowing KIP syntax.