How do I install the Denario skill?

You can install it using the uv package manager with the command 'uv add denario[app]' or via pip.

Which LLM providers does Denario support?

It supports various providers compatible with AG2 and LangGraph, including Google Vertex AI and OpenAI.

Can I use my own research ideas instead of letting the AI generate them?

Yes, Denario supports hybrid workflows where you can provide custom ideas, methodologies, or results for the pipeline to process into a final paper.

Does Denario help with journal formatting?

Yes, it includes a dedicated paper generation stage that produces formatted LaTeX source code tailored to specific journal styles, such as the American Physical Society (APS).

What is Denario's primary function?

Denario is a multiagent AI system that automates the scientific research process, including data analysis, hypothesis generation, methodology development, and LaTeX paper writing.

Denario AI Research Orchestrator

Name: Denario AI Research Orchestrator
Author: Microck

byMicrock

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데이터 과학 및 ML

Automates end-to-end scientific research workflows from initial data analysis and hypothesis generation to the production of publication-ready LaTeX manuscripts.

소개

Denario is a sophisticated multiagent AI system built on AG2 and LangGraph frameworks designed to streamline the entire scientific research lifecycle. It empowers researchers by orchestrating specialized agents to handle complex tasks such as formulating novel hypotheses, developing rigorous methodologies, executing computational experiments, and generating fully formatted LaTeX papers for journals like the APS. Whether starting with raw data or a custom research idea, Denario provides a structured, reproducible pipeline that bridges the gap between data analysis and academic publication through advanced AI orchestration.

주요 기능

81 GitHub stars
Automated hypothesis generation and methodology development from raw datasets
Execution of computational experiments with integrated data visualization
Flexible hybrid workflows allowing for both automated and manual research inputs
Publication-ready LaTeX paper generation with support for specific journal formats
End-to-end multiagent research orchestration using AG2 and LangGraph

사용 사례

Rapidly exploring and testing multiple research hypotheses across various scientific domains
Generating production-grade LaTeX manuscripts that comply with specific journal formatting requirements
Converting raw experimental datasets into structured scientific papers with minimal manual intervention

소개

주요 기능

81 GitHub stars
Automated hypothesis generation and methodology development from raw datasets
Execution of computational experiments with integrated data visualization
Flexible hybrid workflows allowing for both automated and manual research inputs
Publication-ready LaTeX paper generation with support for specific journal formats
End-to-end multiagent research orchestration using AG2 and LangGraph

사용 사례

Rapidly exploring and testing multiple research hypotheses across various scientific domains
Generating production-grade LaTeX manuscripts that comply with specific journal formatting requirements
Converting raw experimental datasets into structured scientific papers with minimal manual intervention