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.

The AI Data Viz Agent leverages large language models to interpret freeform textual instructions, and then orchestrates data processing and plotting libraries to generate code and render visualizations. Users can load datasets in CSV or JSON formats or pass pandas DataFrames directly. Once a dataset is provided, the agent can filter, group, and aggregate data based on prompts like “Show average sales by quarter for top products.” It supports multiple chart types including bar, line, scatter, pie, histogram, and heatmap, with customizable colors, labels, and annotations. The agent runs as a Python package, with optional CLI and API interfaces, enabling integration into notebooks, web services, or automated reporting pipelines.

Chat-With-Data is a Python-based tool and web interface built on Streamlit, LangChain, and OpenAI’s GPT API. It automatically parses tabular datasets or database schemas and creates an AI agent that understands natural language queries about your data. Under the hood, it chunks large tables, builds an embedding index for semantic search, and formulates dynamic prompts to generate context-aware responses. Users ask questions like “What are the top 5 sales regions this quarter?” or “Show me a bar chart of revenue by category,” and receive answers or interactive plots without writing SQL or pandas code. The platform runs locally or on a server, ensuring data privacy while accelerating exploratory analysis for both technical and nontechnical users.