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DATAGEN

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Basic Information

DATAGEN is an AI-driven multi-agent data analysis and research platform designed to automate end-to-end research workflows. It orchestrates specialized agents to generate and validate hypotheses, preprocess and analyze datasets, create visualizations, and compile written reports. The project integrates LangChain, OpenAI GPT models, and LangGraph to build a state graph that coordinates agent tasks and retains context via a Note Taker memory system. The repository provides runnable examples in Jupyter Notebook and a main.py script, requires Python 3.10+, and uses environment configuration for data storage, Conda paths, ChromeDriver, and API keys. It is intended for users who want to run automated research pipelines or customize agent behavior by modifying agent creation and workflow definitions in the notebook. The system emphasizes scalable analysis pipelines and enterprise-oriented automation.

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App URL
https://github.com/starpig1129/DATAGEN
Github Repository
https://github.com/starpig1129/DATAGEN/blob/main/README.md

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App Details

Features
DATAGEN bundles an Advanced Hypothesis Engine for automated hypothesis generation and refinement, enterprise data processing tools for cleaning and transformation, and a Dynamic Visualization Suite that produces interactive charts and report-ready graphics. Its architecture is multi-agent, with components such as hypothesis_agent, process_agent, visualization_agent, code_agent, searcher_agent, report_agent, quality_review_agent, and note_agent to handle specialized tasks. Smart Memory Management is provided by a Note Taker agent to retain context and state across steps. The Adaptive Processing Pipeline enables dynamic workflow adjustments and resource optimization. The project supports Jupyter Notebook workflows and a Python script entrypoint, and exposes configuration through environment variables including OpenAI API key and optional Firecrawl and LangChain keys.
Use Cases
The repository helps researchers and data practitioners automate repetitive and complex parts of empirical work by orchestrating multiple agents to perform hypothesis creation, data cleaning, analysis, visualization, literature and web search, code generation, and report writing. It reduces manual orchestration by providing a coordinated pipeline managed via LangGraph, offers quality review and revision loops, and records process details through a note agent for reproducibility. Users can run example notebooks or the main script to process CSV datasets and customize the workflow to specific tasks. The platform is designed to scale and be production-ready, but it requires an OpenAI API key and sufficient credits, and users are advised to back up data because agents may modify datasets.

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