Does Denario require specific LLM API keys?

Yes, Denario requires API keys from supported providers like Google Vertex AI or OpenAI, as it uses AG2 and LangGraph for agent orchestration.

Does Denario have a user interface?

Yes, Denario includes a web-based graphical user interface that can be launched using the 'denario run' command for interactive workflow management.

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

Which journal formats does Denario support?

It currently supports American Physical Society (APS) formatting, with capabilities for additional journal styles through its extensible LaTeX generation engine.

Can I use Denario for only specific parts of my research?

Yes, Denario supports hybrid workflows where you can provide your own ideas or methodologies while using the AI to automate results and paper generation.

Denario Scientific Research Assistant

Name: Denario Scientific Research Assistant
Author: Activer007

byActiver007

Data Science & ML

Automates the end-to-end scientific research pipeline from hypothesis generation to publication-ready LaTeX manuscripts.

About

Denario is a comprehensive multiagent AI system designed to streamline scientific workflows by orchestrating specialized agents for hypothesis generation, methodology development, computational analysis, and paper writing. Built on the AG2 and LangGraph frameworks, it allows researchers to transition from raw datasets to formatted journal articles through a structured four-stage pipeline. It is particularly useful for scientists and data analysts looking to accelerate the path from data discovery to academic publication while maintaining rigorous methodology and professional formatting.

Key Features

Automated hypothesis generation based on provided data descriptions
Publication-ready LaTeX paper generation for journals like APS
Computational experiment execution and visualization generation
0 GitHub stars
Flexible hybrid workflows supporting both manual and automated inputs
Multiagent orchestration for specialized research and analysis tasks

Use Cases

Automating the execution of data science experiments and finding summaries
Generating novel research hypotheses and methodologies from raw datasets
Converting computational results into professional, journal-formatted LaTeX manuscripts

About

Key Features

Automated hypothesis generation based on provided data descriptions
Publication-ready LaTeX paper generation for journals like APS
Computational experiment execution and visualization generation
0 GitHub stars
Flexible hybrid workflows supporting both manual and automated inputs
Multiagent orchestration for specialized research and analysis tasks

Use Cases

Automating the execution of data science experiments and finding summaries
Generating novel research hypotheses and methodologies from raw datasets
Converting computational results into professional, journal-formatted LaTeX manuscripts