PaperBanana: Automating Research Diagrams With an Agentic AI Framework

Google just shipped a framework that turns natural language into publication-ready figures. Here's how the agentic pipeline actually works, with real code. I want to tell you about the specific kind of frustration that makes researchers consider career changes. You've just finished a three-month experiment. The results are clean, the story is clear and all you need to do is produce the figures for the paper. Six hours later you're on Stack Overflow at 11pm trying to figure out why matplotlib is cutting off your axis labels in the PDF export and the actual insight you were excited about three hours ago feels very far away. PaperBanana is Google AI's answer to this. It's an agentic framework that takes natural language descriptions and produces publication-ready research figures, not rough drafts that need cleanup, but figures you can drop directly into a Nature or NeurIPS submission. The GitHub activity around it has been significant and the architecture underneath deserves attention in