[P] Open-source agentic AI that reasons through data science workflows — looking for bugs & feedback

Hey everyone,
I’m building an open-source agent-based system for end-to-end data science and would love feedback from this community.

Instead of AutoML pipelines, the system uses multiple agents that mirror how senior data scientists work:

• EDA (distributions, imbalance, correlations)
• Data cleaning & encoding
• Feature engineering (domain features, interactions)
• Modeling & validation
• Insights & recommendations

The goal is reasoning + explanation, not just metrics.

It’s early-stage and imperfect — I’m specifically looking for:

• 🐞 bugs and edge cases
• ⚙️ design or performance improvements
• 💡 ideas from real-world data workflows

Demo: https://pulastya0-data-science-agent.hf.space/
Repo: https://github.com/Pulastya-B/DevSprint-Data-Science-Agent

Happy to answer questions or discuss architecture choices.