https://github.com/Agentic-Systems-Lab/rigorous
A comprehensive suite of tools, built to liberate science by making the creation, evaluation, and dissemination of research more transparent, affordable, and efficient.
https://github.com/Agentic-Systems-Lab/rigorous
academic-paper academic-writing ai-agents ai-tools llm manuscript-analyses python research science
Last synced: about 9 hours ago
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A comprehensive suite of tools, built to liberate science by making the creation, evaluation, and dissemination of research more transparent, affordable, and efficient.
- Host: GitHub
- URL: https://github.com/Agentic-Systems-Lab/rigorous
- Owner: Agentic-Systems-Lab
- License: mit
- Created: 2025-04-09T09:45:37.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-08-08T22:24:23.000Z (11 months ago)
- Last Synced: 2025-09-01T16:49:00.113Z (10 months ago)
- Topics: academic-paper, academic-writing, ai-agents, ai-tools, llm, manuscript-analyses, python, research, science
- Language: Python
- Homepage: https://rigorous.review
- Size: 184 KB
- Stars: 210
- Watchers: 6
- Forks: 14
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
- awesome-ai-for-economists - Rigorous - Open-source AI pre-submission peer review generating referee-style manuscript feedback using your own LLM keys.  (Academic Writing and LaTeX / Economic Data Sources)
README
# Rigorous AI-Powered Scientific Manuscript Analysis
> **v0.2 Now Live:** The latest version of the AI Reviewer (v0.2) is now available at [https://www.rigorous.review/](https://www.rigorous.review/). Upload your manuscript, provide context on your target journal and receive structured feedback directly online via an interactive interface — now with progress tracking built in. Once initial testing of v0.2 is complete, we will make all module prompts open source to promote transparency and enable community contributions.
> **Help Us Improve!** Please provide feedback via [this short feedback form](https://docs.google.com/forms/d/1EhQvw-HdGRqfL01jZaayoaiTWLSydZTI4V0lJSvNpds) to help us improve the system.
> Support AI Reviewer v0.3 by rocking some *peer-reviewed merch* 👕🧠 – [grab yours here](https://rigorous-shop.fourthwall.com/) – GitHub contributors get free gear.
## Vision
This repository is intended for tools that make the creation, evaluation, and distribution of scientific knowledge more transparent, cheaper, faster, and better. Let's build this future together!
## Project Structure
- **Agent1_Peer_Review**: Multiagent AI review system for comprehensive manuscript analysis, detailed feedback, and PDF report generation (v0.1).
- **Agent2_Outlet_Fit**: (In Development) Tool for evaluating manuscript fit with target journals/conferences.
## Current Status
### Active Tools
- **Agent1_Peer_Review**: ✅ v0.1 Ready for use!
- Comprehensive manuscript analysis with specialized agents
- Detailed feedback on sections, scientific rigor, and writing quality (including quality control loops)
- JSON output with actionable recommendations
- PDF report generation
- [📄 Detailed Documentation and Key Areas for Contribution](https://github.com/robertjakob/rigorous/blob/main/Agent1_Peer_Review/README.md)
### In Development
- **Agent2_Outlet_Fit**: 🚧 In Development
- Core functionality being implemented
- Integration with Agent1_Peer_Review in progress
- Testing and validation ongoing
- [🛠️ Development Plan](https://github.com/robertjakob/rigorous/blob/main/Agent2_Outlet_Fit/README.md)
### Future Modules and Ideas
- **Embedding-based similarity analysis** (by [@andjar](https://github.com/andjar)): Use embeddings (as in [*The landscape of biomedical research*](https://github.com/berenslab/pubmed-landscape)) to compare a paper’s abstract with existing literature. This could help surface uncited but relevant work and suggest suitable journals based on similarity clusters.
- Support for Drafting Reviewer Reponses.
- Feedback on Research Proposals and Protocols.
- AI-enabled document creation tool ("Cursor for Papers").
## Requirements
- Python 3.7+
- OpenAI API key (the system can be adapted to alternative LLMs, including locally hosted ones)
- PDF manuscripts to analyze
- Dependencies listed in each tool's requirements.txt
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## Citation
If you use the Rigorous AI Reviewer in your research or project, please cite:
```bibtex
@software{rigorous_ai_reviewer2025,
author = {Jakob, Robert and O'Sullivan, Kevin},
title = {Rigorous AI Reviewer: Enabling AI for Scientific Manuscript Analysis},
year = {2025},
publisher = {GitHub},
url = {https://github.com/robertjakob/rigorous}
}
```
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