Denario is a multiagent AI system designed to automate the entire scientific research lifecycle, from initial data analysis to writing publication-ready LaTeX papers.

Which journal formats does Denario support?

Currently, Denario supports the American Physical Society (APS) format, with the architecture built to support additional journal-specific styles.

Do I need specific API keys to use Denario?

Yes, Denario requires API keys from supported LLM providers such as Google Vertex AI or OpenAI to power its internal multiagent orchestration.

Can Denario execute code to generate results?

Yes, Denario can execute computational experiments, perform data analysis using libraries like pandas and sklearn, and automatically create visualizations for your research.

How do I install Denario for my project?

You can install Denario using 'uv add denario[app]' or via pip. It requires Python 3.12 or higher and can also be deployed via Docker for consistent LaTeX compilation.

Denario Scientific Research Automation

Name: Denario Scientific Research Automation
Author: Activer007

byActiver007

データサイエンスとML

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

概要

Denario is a sophisticated multiagent AI system designed to streamline the academic and scientific research process by orchestrating specialized agents through AG2 and LangGraph. It assists researchers in analyzing datasets, formulating novel hypotheses, developing rigorous methodologies, and executing computational experiments. Beyond analysis, Denario automates the technical writing process, generating journal-formatted papers in LaTeX complete with integrated visualizations and references, making it an indispensable tool for accelerating scientific discovery and publication.

主な機能

Hypothesis and methodology generation based on raw data descriptions
Computational experiment execution with integrated data visualization
Interactive GUI for managing complex research workflows and agent tasks
Automated multiagent orchestration for end-to-end research pipelines
Publication-ready LaTeX paper generation for specific scientific journals
0 GitHub stars

ユースケース

Generating novel research ideas and hypotheses from raw experimental datasets
Developing structured methodologies for computational experiments and data analysis
Automating the writing and formatting of journal papers in LaTeX from research results

概要

主な機能

Hypothesis and methodology generation based on raw data descriptions
Computational experiment execution with integrated data visualization
Interactive GUI for managing complex research workflows and agent tasks
Automated multiagent orchestration for end-to-end research pipelines
Publication-ready LaTeX paper generation for specific scientific journals
0 GitHub stars

ユースケース

Generating novel research ideas and hypotheses from raw experimental datasets
Developing structured methodologies for computational experiments and data analysis
Automating the writing and formatting of journal papers in LaTeX from research results