KnowledgeClaw: Towards Agentic Knowledge Engineering via Omnimodal Hypergraphs

Knowledge engineering (KE) turns raw files into a reasoning-ready knowledge base, a task now within reach of autonomous LLM agents. KE has progressed from chunk and graph retrieval-augmented generation (RAG) to memory agents and agentic, multimodal KE systems. However, a live knowledge base needs omnimodality, a closed-loop lifecycle and multi-granular application at once, yet each existing system satisfies at most two and no benchmark measures all three. We formalize Omnimodal Knowledge Engineering (OmniKE) and release OmniKE-Bench, six tasks with 1,829 model-free items over the real files of ten personas in five modalities. We propose KnowledgeClaw, an agentic system that builds an omnimodal typed hypergraph, maintains it through a closed-loop lifecycle agent and routes each query to the granularity it needs. Against 30 baselines, KnowledgeClaw ranks first on all six tasks with an OKE of 55.9, +14.6 over the strongest baseline. Our software and data are publicly available.