# zi-yue-1129/DATAGEN

DATAGEN: AI-driven multi-agent research assistant automating hypothesis generation, data analysis, and report writing.

Repository: https://github.com/zi-yue-1129/DATAGEN
Canonical: https://ross.abutalabs.com/products/datagen
Language: Python
License: MIT
License Family: permissive
Topics: artificial-intelligence, data-analysis, data-analytics, data-science, langchain, langgraph, large-language-model, large-language-models, multiagent-systems, python, ai-data-analysis, ai, code-generation, agent, llm
Last push: 2026-08-16T08:38:07+00:00

## Health v2 (maintenance only)
Score: 67/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 98, release rhythm 35, longevity 55
- inputs: {"age_days": 771, "days_push": 17, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1793, forks 246 (observed 2026-08-28T04:05:37.246377+00:00)

## What it is
DATAGEN is an AI-powered multi-agent research and data analysis platform built with LangChain, LangGraph, and OpenAI GPT models. It automates hypothesis generation, data cleaning and analysis, visualization, and report writing through coordinated specialized agents.

## Use cases
- automate exploratory data analysis with AI agents
- generate research hypotheses from a dataset
- produce analysis reports and charts automatically
- run a multi-agent data science pipeline
- analyze csv data and get insights without coding
- build an AI research assistant workflow

## When to choose
- you want end-to-end automated data analysis and report generation
- you need hypothesis generation and validation driven by LLMs
- you want a Python-based multi-agent system built on LangGraph
- you prefer a self-hosted research assistant you can customize

## When to avoid
- you need fully deterministic, auditable analysis without LLM involvement
- you cannot share your data with external LLM APIs
- you need a lightweight single-script analysis tool
- you require strict enterprise compliance guarantees out of the box

## Facets
- artifact type: application
- maturity: active
- function: agent-framework, data-science, data-visualization, llm-inference, chatbot
- domain: artificial-intelligence, data-science, large-language-models, data-visualization, analytics
- platform: python, cross-platform
- tags: multi-agent, langchain, langgraph, hypothesis-generation, automated-reporting, research-assistant, openai, ai-agents

## Member repositories
- zi-yue-1129/DATAGEN (main) score 67

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:37.246377+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:22:57.067580+00:00, confidence not recorded.
  - readme: https://github.com/zi-yue-1129/DATAGEN (fetched 2026-08-28T04:05:37.246377+00:00, sha 4e98b39bae42)
- Data as of 2026-08-30T08:39:29.467469+00:00.