Product updates, research, and ideas for building better agents.
We built Knowhere as a memory layer between complex, messy documents and AI agents. Rather than stopping at text extraction, it converts documents into structured memory that can be updated and reused.
Instead of aggressively converting every document into stripped-down text chunks, keep the original visual pages intact so the AI agent can inspect the actual layout, check figures against the source, and provide verifiable evidence.
Knowhere Notebook and MCP let agents in Cursor, Claude, and Codex search a shared cloud document Brain.
Production RAG quality is mostly evaluation and data/state work—not model swaps.
Knowhere is post-parse agent memory—tree hierarchy, multimodal assets, graph, and agentic retrieval—not a MinerU replacement.
Document agents need persistent world state, not just retrieved observations.
