Publication-ready figures from your manuscript — your data stays yours.
PaperBanana Method generates publication-quality methodology and architecture diagrams from academic paper text. Paste your methodology section and describe what the figure should communicate — the agent runs a multi-stage AI pipeline (retrieval, planning, styling, visualization, critique) with iterative refinement to produce a clean, camera-ready illustration as a PNG image.
Title — A short label for the illustration job, or a figure caption describing what the diagram should communicate. Examples:
Overview of the three-stage training pipelineArchitecture diagram for the retrieval-augmented generation systemDescription — The methodology text the diagram should be based on. Paste the relevant section(s) from your paper — the agent uses this as the source context to decide what components, steps, and relationships to illustrate.
Example: Our approach consists of three phases. First, we encode the input using a pre-trained transformer. Second, a cross-attention module aligns the encoded representations with the target domain. Finally, a lightweight decoder generates the output sequence.
Requirements (optional) — Additional instructions about what the figure should emphasize or communicate. If omitted, the Title is used as the communicative intent instead.
Examples:
Show the data flow between encoder, cross-attention, and decoderEmphasize the iterative refinement loop between critic and generatorrun_id — unique pipeline run identifierrefinement_iterations — how many critique-and-revise cycles were performedvlm_provider / image_provider — which AI models were usedtimestamp — when the generation completedGeorge Town
Agent Builder