This project implements a sophisticated multi-agent system for analyzing academic manuscripts. The system uses a combination of section-specific, rigor, and writing quality agents to provide detailed feedback and suggestions for improvement. Each agent specializes in a specific aspect of manuscript analysis and provides structured JSON output.

Agent Structure

The system consists of three main categories of agents:

Section Agents (S1-S10)

S1: Title and Keywords Analysis
S2: Abstract Review
S3: Introduction Assessment
S4: Literature Review Analysis
S5: Methodology Evaluation
S6: Results Analysis
S7: Discussion Review
S8: Conclusion Assessment
S9: References Analysis
S10: Supplementary Materials Review

Rigor Agents (R1-R7)

R1: Originality and Contribution
R2: Impact and Significance
R3: Ethics and Compliance
R4: Data and Code Availability
R5: Statistical Rigor
R6: Technical Accuracy
R7: Consistency

Writing Agents (W1-W8)

W1: Language and Style
W2: Narrative and Structure
W3: Clarity and Conciseness
W4: Terminology Consistency
W5: Inclusive Language
W6: Citation Formatting
W7: Target Audience Alignment
W8: Visual Presentation

Installation

Clone the repository
Install dependencies:

pip install -r requirements.txt

Usage

Place your manuscript PDF in the manuscripts/ directory
Run the analysis:

python run_analysis.py

Output

The system generates JSON files in the results/ directory containing:

Individual agent results ({agent_name}_results.json)
Combined results (combined_results.json)
Manuscript data (manuscript_data.json)

Each agent's analysis follows a consistent JSON structure:

{
    "score": int,  // Score from 1-5
    "critical_remarks": [
        {
            "category": str,
            "location": str,
            "issue": str,
            "severity": str,
            "impact": str
        }
    ],
    "improvement_suggestions": [
        {
            "location": str,
            "category": str,
            "focus": str,
            "original_text": str,
            "improved_version": str,
            "explanation": str
        }
    ],
    "detailed_feedback": {
        // Agent-specific detailed analysis
    },
    "summary": str  // Overall assessment summary
}

Configuration

Environment variables are managed in .env
Agent configurations can be modified in src/core/config.py
Model settings can be adjusted in src/core/config.py

Development

Project Structure

V6_multi_agent3/
├── src/
│   ├── reviewer_agents/
│   │   ├── section/      # Section agents (S1-S10)
│   │   ├── rigor/        # Rigor agents (R1-R7)
│   │   ├── writing/      # Writing agents (W1-W8)
│   │   └── controller_agent.py
│   ├── core/            # Core functionality and configuration
│   └── utils/           # Utility functions
├── manuscripts/         # Input manuscripts
├── results/            # Analysis results
└── tests/             # Test suite

Adding New Agents

Create a new agent class inheriting from BaseReviewerAgent
Implement the required analysis method
Add the agent to the controller's agent dictionary

Testing

Run the test suite:

pytest tests/

License

MIT License

Contributing

Fork the repository
Create a feature branch
Commit your changes
Push to the branch
Create a Pull Request

For detailed guidelines on how to contribute, please see CONTRIBUTING.md.

Join the Project

We Need Your Help! This is Version 6.0 - a work in progress, which means:

Expect imperfections: The system is continuously being improved
Your expertise matters: Help us improve agent accuracy, especially specialized agents
Key areas for contribution:
- Developing specialized agents for different research fields
- Improving prompt engineering for existing agents
- Enhancing analysis accuracy
- Adding support for different document formats
- Implementing more sophisticated error detection

Share your feedback: Contact us at rjakob@ethz.ch with your experiences and suggestions

Use more powerful models: The default implementation uses GPT-4.1-nano for accessibility, but you can configure the system to use more sophisticated models with your own API keys.

Together, we can build the best review agent team and improve the quality of scientific publishing!