Datagen — AI Agent Framework: Live Stats & TrendScore

Live GitHub stats, community sentiment, and trend data for Datagen. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: Apr 27, 2026 • Sentiment updated: May 8, 2026

GitHub Statistics

Community Sentiment

Community Buzz: As title suggested, we wrote a tutorial on how to use Claude Code with Skill from your Clay table here: https://datagen.dev/agents-guide/clay-claude-code- from Reddit, and 'Datagen has NO HAIR!' from X/Twitter

Pros & Cons

What People Love

Reddit users praise datagen for its usefulness, X/Twitter users enjoy datagen's design

Common Complaints

datagen being scrapped, technical issues with datagen

Biggest Positive: datagen useful

Biggest Negative: datagen scrapped

Why Datagen Stands Out

DATAGEN stands out from alternatives due to its innovative multi-agent architecture and intelligent automation capabilities, which enable advanced hypothesis generation, data analysis, and report writing. The project's use of cutting-edge technologies such as LangChain, OpenAI's GPT models, and LangGraph allows for optimal performance and adaptability. By leveraging these technologies, DATAGEN solves the problem of streamlining complex research processes, making it an attractive solution for researchers and data analysts.

Built With

Build an automated research assistant that generates hypotheses and writes reports — DATAGEN's multi-agent system enables this by automating data analysis, visualization, and report generation, Build a data analysis pipeline that integrates with large language models — DATAGEN's LangChain and LangGraph integration allows for seamless interaction with LLMs, Build a customizable research workflow that adapts to complex analysis requirements — DATAGEN's intelligent task distribution and coordination enable this, Build an enterprise-grade data analysis platform that handles large datasets — DATAGEN's scalable architecture and robust data processing capabilities enable this, Build an AI-driven data visualization tool that creates interactive and custom reports — DATAGEN's dynamic visualization suite enables this

Getting Started

  1. Clone the repository using `git clone https://github.com/starpig1129/DATAGEN.git`
  2. Create and activate a Conda virtual environment using `conda create -n datagen python=3.10` and `conda activate datagen`
  3. Install dependencies using `pip install -r requirements.txt`
  4. Set up environment variables by renaming `.env Example` to `.env` and filling in the required values
  5. Try running the system using `python main.py` to verify it works

About

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

Category & Tags

Category: multi-agent

Tags: agent, ai, ai-data-analysis, artificial-intelligence, code-generation, data-analysis, data-analytics, data-science, langchain, langgraph, large-language-model, large-language-models, llm, multiagent-systems, python

Market Context

datagen is compared to other projects like Crash Dummy