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PaperBanana - Is this the BEST Agentic Framework for Generating COMPLEX Images?
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PaperBanana - Is this the BEST Agentic Framework for Generating COMPLEX Images?

Introduction to Paper Banana

Overview of the New Tool

  • A new paper titled "Paper Banana" from a collaboration between Google and Peking University generates publication-quality diagrams from plain text descriptions.
  • Unlike existing tools like Nano Banana Pro, which generate images in one step, Paper Banana employs a multi-agent system for enhanced output quality.

The Multi-Agent Approach

  • Paper Banana utilizes five AI agents: one for retrieving reference examples, another for planning layout, a third for applying design guidelines, a fourth for generating images, and the last for critiquing results.
  • This process creates a "generate, critique, refine" loop that improves the final output significantly compared to single-step models.

Demonstration of Output Quality

Visual Output Analysis

  • The speaker showcases an example generated by Paper Banana, highlighting its detailed architecture representation through color coding and clear directional arrows.
  • The diagram effectively explains complex connections within the architecture while maintaining clarity in annotations and data flow.

Disclaimer on Implementation

  • The demonstration used an unofficial community-built open-source implementation rather than the official code from Google and Peking University.
  • The official code is expected to be released soon; viewers are encouraged to express interest in further videos once it becomes available.

Setting Up Paper Banana

Cloning and Configuration

  • Users can clone the community-based GitHub repository of Paper Banana and set up their API keys to start using it.
  • Key components include various agents such as visualizer, stylist, retriever, planner, critic, and base agent working collaboratively under Gemini 3 Banana Pro model.

Running Commands

  • Users are guided on how to run commands with sample inputs to create diagrams using Paper Banana's transformer model.
  • After executing initial commands with sample input, users can provide detailed prompts to see how outputs vary based on input complexity.

Iterative Generation Process

Diagram Creation Workflow

  • The tool retrieves relevant references before executing detailed planning exercises followed by iterative refinements across three generations.
  • Each iteration produces different versions of diagrams showcasing improvements until reaching a final output that self-corrected based on previous iterations.

Conclusion on Effectiveness

  • This iterative approach demonstrates how multiple feedback loops enhance diagram generation quality beyond what traditional image generation models can achieve.

Creating a Sophisticated Agent Diagram with Google's ADK

Overview of the Task

  • The speaker initiates a new test by prompting an AI to create a text file named ADK agent.ext, detailing an agent system built on Google's ADK kit.
  • The task involves generating a sophisticated diagram for Google’s Agent Development Kit (ADK), indicating the speaker's familiarity with the framework.

Understanding Generated Outputs

  • After providing input, the AI generates outputs in multiple iterations, showcasing its ability to create complex structures based on user prompts.
  • The final output includes various agents: an orchestrator agent, research agent, data analysis agent leveraging Bitquery, and a report generator.

Key Features of the Generated Diagram

  • The generated diagram illustrates a multi-agent architecture using Google ADK, highlighting components like persistent memory storage and structured business reports.
  • It emphasizes real-time information access and mentions tools such as Pandas for better data structuring.

Significance of Results

  • The speaker notes that the AI's output could be suitable for inclusion in academic papers, demonstrating high-quality results from detailed prompts.

Performance Metrics from Paper Banana

  • Discussing performance metrics, Vanilla Nano Banana Pro scored 43.2 overall while Paper Banana achieved 60.2 across four dimensions: faithfulness, conciseness, readability, and aesthetics.
  • Notably, Paper Banana outperformed human-drawn diagrams in conciseness, readability, and aesthetics; however, humans still excelled in faithfulness due to precise intent understanding.

Implications for Future Workflows

  • This development represents advanced "agentic AI," where specialized agents collaborate creatively—an approach expected to shape future AI systems beyond 2026.
  • The potential applications extend beyond researchers to professionals like solution architects and product managers who need effective visual representations of complex ideas.

Upcoming Developments

  • The official code release is anticipated soon following initial announcements; viewers are encouraged to share use cases for testing once available.
Video description

Paper Banana: AI Multi-Agent System for Publication-Quality Diagrams | Complete Tutorial If you came here searching for "Google PaperBanana", "PaperBanana" or "What is PaperBanana", then you are in the right place! DISCLAIMER: All opinions are my own and do not belong to my employer Discover Paper Banana, a groundbreaking multi-agent AI system from Google and Peking University that generates publication-quality diagrams from plain text descriptions. Unlike single-model approaches, Paper Banana uses 5 specialized AI agents working together to create, critique, and refine technical diagrams with stunning accuracy. 🎯 What You'll Learn: How Paper Banana's 5-agent architecture works (Retriever, Planner, Stylist, Visualizer, and Critic) Why multi-agent systems outperform single-model approaches like Nano Banana Pro Step-by-step setup using the unofficial open-source implementation Live demo: Generating a Transformer architecture diagram Custom example: Creating an ADK (Agent Development Kit) system diagram Performance benchmarks: Paper Banana vs. human-drawn diagrams ⏱️ Timestamps: 0:00 - Introduction to Paper Banana 0:43 - How Paper Banana Works 1:28 - Demo: Transformer Architecture 2:35 - Setup and Installation 3:59 - Running the First Example 4:48 - Results and Iterations 6:08 - Custom Example: ADK Agent System 7:26 - Analyzing the ADK Diagram 9:14 - Performance Benchmarks 10:00 - Why This Matters 10:45 - Wrap Up and Next Steps 📊 Key Results: Paper Banana scored 60.2 overall vs. 43.2 for vanilla Nano Banana Pro Beats human-drawn diagrams in conciseness, readability, and aesthetics Self-correcting through multiple iterations for optimal results 🔗 Resources: Unofficial GitHub Repository: https://github.com/llmsresearch/paperbanana Original Research Paper: https://arxiv.org/abs/2601.23265 💡 Who Should Watch This: Solution Architects Product Managers Startup Founders Developers Researchers Anyone creating technical diagrams, flowcharts, or system designs 🚀 What Makes Paper Banana Different: Instead of prompting one AI model directly, Paper Banana orchestrates 5 specialized agents: Retriever Finds relevant reference examples Planner Designs the overall layout Stylist Applies design guidelines Visualizer Generates the image (using Nano Banana Pro) Critic Reviews and requests revisions This generate-critique-refine loop produces diagrams that rival professional human work. ⚠️ Important Note: This tutorial uses an unofficial community implementation. The official code from Google and Peking University is expected to release soon. Drop a comment if you want a follow-up video when the official version launches! 💬 Your Turn: What diagram would you want Paper Banana to generate? Drop your use cases in the comments, and I'll test the top requests when the official code launches! 🔔 Subscribe for more enterprise AI tutorials and cutting-edge AI tool reviews! AI #MachineLearning #AgenticAI #PaperBanana #DiagramGeneration #MultiAgent #GoogleAI #TechTutorial #AITools #SystemDesign