Hypogenic

This automates hypothesis generation and testing for empirical research by having LLMs generate 10-20+ testable hypotheses from your datasets in minutes. It supports three approaches: pure data-driven generation, literature-plus-data integration (with PDF processing), and combined methods. The framework showed 8.97% improvement over few-shot baselines in testing and 80-84% hypothesis diversity. You'd use this for research domains like deception detection, AI content identification, or mental health analysis where you need systematic hypothesis exploration. Redis caching cuts API costs during iterative refinement, and it works with both API-based models and local LLMs. The configuration requires careful prompt template setup, but the examples in the HypoGeniC datasets repo give you working templates to adapt.