DataAgent leverages advanced AI agents built on top of LLMs to explore datasets, generate insights, and assemble machine learning pipelines automatically. Users point DataAgent at a CSV, SQL table, or Pandas DataFrame and pose questions in natural language. The agent interprets queries, executes analysis code, visualizes results, and even writes modular Python scripts for ETL and modeling tasks. It streamlines the entire data science workflow by reducing boilerplate coding and accelerating experimentation.

Data Analysis LLM Agent is a self-hosted Python package that integrates with OpenAI and other LLM APIs to automate end-to-end data exploration workflows. Upon providing a dataset (CSV, JSON, Excel, or database connection), the agent generates code for data cleaning, feature engineering, exploratory visualization (histograms, scatter plots, correlation matrices), and statistical summaries. It interprets natural language queries to dynamically run analyses, update visuals, and produce narrative reports. Users benefit from reproducible Python scripts alongside conversational interaction, enabling both programmers and non-programmers to derive insights efficiently and compliantly.