Can I provide my own research hypotheses to Denario?

Yes, while Denario can auto-generate ideas from your data, you can also manually set your own custom research ideas and methodologies.

Does Denario require specific programming environments?

It requires Python 3.12 or higher and recommends using 'uv' for package management or Docker for a pre-configured LaTeX environment.

What journal formats does the paper generation support?

Denario currently supports American Physical Society (APS) formatting, with the ability to generate complete LaTeX source code for publication.

What frameworks does Denario use for agent orchestration?

Denario utilizes the AG2 and LangGraph frameworks to coordinate multiple specialized AI agents across the research pipeline.

Denario AI Scientific Research Assistant

Name: Denario AI Scientific Research Assistant
Author: ricable

byricable

•

데이터 과학 및 ML

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

소개

Denario is a sophisticated multiagent AI system designed to streamline the entire lifecycle of scientific research. Built on the AG2 and LangGraph frameworks, it orchestrates specialized agents to handle complex tasks including data-driven hypothesis generation, methodology development, computational experiment execution, and the creation of journal-formatted LaTeX papers. This skill is ideal for researchers and data scientists who need to accelerate their discovery process, automate repetitive analysis tasks, and ensure highly structured, reproducible research outputs with minimal manual intervention.

주요 기능

Automated research hypothesis generation from raw datasets
End-to-end pipeline from data description to results visualization
Flexible hybrid workflows allowing both automated and custom manual inputs
1 GitHub stars
Publication-ready LaTeX paper generation with support for APS journal styles
Multiagent orchestration using AG2 and LangGraph for specialized research tasks

사용 사례

Generating novel research questions and structured methodologies from time-series datasets
Automating the drafting of academic papers including integrated figures and computational findings
Executing and documenting reproducible computational research experiments through a standardized pipeline

소개

주요 기능

Automated research hypothesis generation from raw datasets
End-to-end pipeline from data description to results visualization
Flexible hybrid workflows allowing both automated and custom manual inputs
1 GitHub stars
Publication-ready LaTeX paper generation with support for APS journal styles
Multiagent orchestration using AG2 and LangGraph for specialized research tasks

사용 사례

Generating novel research questions and structured methodologies from time-series datasets
Automating the drafting of academic papers including integrated figures and computational findings
Executing and documenting reproducible computational research experiments through a standardized pipeline