ai-data-science-team
<div align="center"> <a href="https://github.com/business-science/ai-data-science-team"> <picture> <img src="./img/ai_data_science_logo.png" alt="AI Data Science Team" width="360"> </picture> </a> </div> <div align="center"> <em>AI Data Science Team + AI Pipeline Studio</em> </div> <div align="center"> <a href="https://pypi.python.org/pypi/ai-data-science-team"><img src="https://img.shields.io/pypi/v/ai-data-science-team.svg?style=for-the-badge" alt="PyPI"></a> <a href="https://github.com/business-science/ai-data-science-team"><img src="https://img.shields.io/pypi/pyversions/ai-data-science-team.svg?style=for-the-badge" alt="versions"></a> <a href="https://github.com/business-science/ai-data-science-team/blob/main/LICENSE"><img src="https://img.shields.io/github/license/business-science/ai-data-science-team.svg?style=for-the-badge" alt="license"></a> <img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/business-science/ai-data-science-team?style=for-the-badge"> </div> # AI Data Science Team AI Data Science Team is a Python library of specialized agents for common data science workflows, plus a flagship app: **AI Pipeline Studio**. The Studio turns your work into a visual, reproducible pipeline, while the AI team handles data loading, cleaning, visualization, and modeling. **Status:** Beta. Breaking changes may occur until 0.1.0. [**Please ⭐ us on GitHub (it takes 2 seconds and means a lot).**](https://github.com/business-science/ai-data-science-team) ## AI Pipeline Studio (Flagship App) AI Pipeline Studio is the main example of the AI Data Science Team in action.  Highlights: - Pipeline-first workspace: Visual Editor, Table, Chart, EDA, Code, Model, Predictions, MLflow - Manual + AI steps with lineage and reproducible scripts - Multi-dataset handling and merge workflows - Project saves: metadata-only or full-data - Storage footprint controls and rehydrate workflows Run it: ```bash streamlit run apps/ai-pipeline-studio-app/app.py ``` Full app docs: `apps/ai-pipeline-studio-app/README.md` ## Quickstart ### Requirements - Python 3.10+ - OpenAI API key (or Ollama for local models) ### Install the app and library Clone the repo and install in editable mode: ```bash pip install -e . ``` ### Run the AI Pipeline Studio app ```bash streamlit run apps/ai-pipeline-studio-app/app.py ``` ## Library Overview The repository includes both the **AI Pipeline Studio** app and the underlying **AI Data Science Team** library. The library provides agent building blocks and multi-agent workflows for: - Data loading and inspection - Cleaning, wrangling, and feature engineering - Visualization and EDA - Modeling and evaluation (H2O + MLflow tools) - SQL database interaction ### Agents (Snapshot) Agent examples live in `examples/`. Notable agents: - Data Loader Tools Agent - Data Wrangling Agent - Data Cleaning Agent - Data Visualization Agent - EDA Tools Agent - Feature Engineering Agent - SQL Database Agent - H2O ML Agent - MLflow Tools Agent - Multi-agent workflows (e.g., Pandas Data Analyst, SQL Data Analyst) - Supervisor Agent (oversees other agents) - Custom tools for data science tasks ## Apps See all apps in `apps/`. Notable apps: - AI Pipeline Studio: `apps/ai-pipeline-studio-app/` - EDA Explorer App: `apps/exploratory-copilot-app/` - Pandas Data Analyst App: `apps/pandas-data-analyst-app/` ## Use OpenAI ```python from langchain_openai import ChatOpenAI llm = ChatOpenAI( model_name="gpt-4.1-mini", ) ``` ## Use Ollama (Local LLM) ```bash ollama serve ollama pull llama3.1:8b ``` ```python from langchain_ollama import ChatOllama llm = ChatOllama( model="llama3.1:8b", ) ``` ## Next-Gen AI Agentic Workshop Want to learn how to build AI agents and AI apps for real data science workflows? Join my next‑gen AI workshop: https://learn.business-science.io/ai-register