How does Denario handle LaTeX compilation?

Denario generates complete LaTeX source files. To compile them into PDFs, you can use a local TeX distribution or the provided Docker image which comes with LaTeX pre-installed.

Can I provide my own research hypotheses?

Absolutely. While Denario can generate novel ideas from data, you can also manually set custom research ideas or methodologies for the agents to execute.

Do I need a specific Python version?

Yes, Denario requires Python 3.12 or higher to ensure compatibility with modern AI frameworks like AG2 and LangGraph.

Which LLM providers are supported?

Denario supports Google Vertex AI, OpenAI, and other LLM services compatible with the AG2 and LangGraph orchestration frameworks.

What journals does Denario support?

Denario currently supports the American Physical Society (APS) format and provides tools to extend support to other journal styles via its customizable templates.

Denario Scientific Research Assistant

Name: Denario Scientific Research Assistant
Author: Microck

byMicrock

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Ciencia de Datos y ML

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

Acerca de

Denario is a sophisticated multiagent AI system designed to streamline the academic research lifecycle. Leveraging AG2 and LangGraph frameworks, it orchestrates specialized agents to analyze datasets, formulate research methodologies, execute computational experiments, and generate formatted manuscripts for major journals. It is particularly useful for researchers looking to accelerate their discovery process, ensure reproducibility, and automate the tedious aspects of paper formatting and literature synthesis through a structured, four-stage research pipeline.

Características Principales

Automated hypothesis generation and methodology development
Interactive GUI for managing complex research workflows
Publication-ready LaTeX manuscript generation for major journals
Execution of computational experiments and data visualization
81 GitHub stars
Multiagent orchestration for autonomous research pipelines

Casos de Uso

Automating the generation of journal-formatted research papers from raw analysis
Accelerating preliminary data exploration and hypothesis testing
Managing structured and reproducible computational experiments

Acerca de

Características Principales

Automated hypothesis generation and methodology development
Interactive GUI for managing complex research workflows
Publication-ready LaTeX manuscript generation for major journals
Execution of computational experiments and data visualization
81 GitHub stars
Multiagent orchestration for autonomous research pipelines

Casos de Uso

Automating the generation of journal-formatted research papers from raw analysis
Accelerating preliminary data exploration and hypothesis testing
Managing structured and reproducible computational experiments