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.
When people say that the 21st century will be the century of biology, the point is not necessarily that biology will replace AI. It may be that AI will increasingly learn from biology.
Learn how to choose a PDF parser API for AI agents and RAG: accuracy, layout structure, latency, cost, and integration trade-offs that matter in production.
Production RAG quality is mostly evaluation and data/state work—not model swaps.
