Show HN: AI Peer Reviewer – Multiagent System for Scientific Manuscript Analysis

Cloud Version Available: A cloud version of the AI Peer Reviewer is now available at https://www.rigorous.company/. Simply upload your manuscript, provide context on target journal and review focus, and receive a comprehensive PDF report via email within 1-2 working days. The cloud version is currently free for testing purposes.

This repository contains tools for making scientific publishing more transparent, cheaper, faster, and ensuring rigorous peer and AI review.

• Agent1_Peer_Review: Advanced peer review system with specialized agents for comprehensive manuscript analysis, detailed feedback, and professional PDF report generation.
• Agent2_Outlet_Fit: (In Development) Tool for evaluating manuscript fit with target journals/conferences.

• Agent1_Peer_Review: ✅ Ready for use
  • Comprehensive manuscript analysis with specialized agents
  • Detailed feedback on sections, scientific rigor, and writing quality
  • JSON output with actionable recommendations
  • PDF report generation (see below)

• Agent2_Outlet_Fit: 🚧 In Development
  • Core functionality being implemented
  • Integration with Agent1_Peer_Review in progress
  • Testing and validation ongoing

This project includes a PDF report generator that creates a professional peer review report based on the outputs of the review agents.

1. Ensure you have the required dependencies installed:

   • reportlab
   • pillow
   • (Other dependencies as listed in requirements.txt)

2. Make sure the following files are present and up to date:

   • executive_summary.json (executive summary and overall scores)
   • quality_control_results.json (detailed section, rigor, and writing results)
   • logo.png (logo for the report header)

3. Run the PDF generator script:

   python Agent1_Peer_Review/pdf_generator.py

4. The generated PDF will be saved to:

   • Agent1_Peer_Review/results/review_report.pdf

• Cover page with logo, manuscript title, and overall scores
• Executive summary and detailed analysis pages for all assessment items (S1–S10, R1–R7, W1–W7)
• Visually appealing tables for scores and suggestions
• Professional layout, color coding, and consistent formatting

For more details, see the comments in Agent1_Peer_Review/pdf_generator.py.

• Python 3.7+
• OpenAI API key
• PDF manuscripts to analyze
• Dependencies listed in each tool's requirements.txt

This project is licensed under the MIT License.

Contributions are welcome! Please feel free to submit a Pull Request.