Denario is a multiagent AI system designed as a Claude Code skill to automate the scientific research lifecycle, from initial data analysis and hypothesis generation to publishing LaTeX papers.

How does Denario handle experimental results?

The system can execute computational experiments using libraries like pandas, sklearn, and matplotlib to generate visualizations and research findings automatically based on your methodology.

Does Denario require specific programming knowledge?

Denario requires Python 3.12 or higher. While it offers a structured Python API for developers, it also includes a web-based GUI for interactive research management.

Can I use Denario for specific academic journals?

Yes, Denario supports multiple journal formats, including the American Physical Society (APS), and generates complete, publication-ready LaTeX source code.

What LLMs are supported by Denario?

Denario supports various LLM providers including Google Vertex AI, OpenAI, and other services compatible with AG2 and LangGraph frameworks.

Denario Scientific Research Assistant

Name: Denario Scientific Research Assistant
Author: Microck

byMicrock

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Data Science & ML

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

About

Denario is a comprehensive multiagent AI system designed to streamline the scientific discovery process and academic publication pipeline. Built on frameworks like AG2 and LangGraph, it coordinates specialized agents to handle complex research tasks including hypothesis generation, methodology design, computational experimentation, and journal-specific paper writing. By bridging the gap between raw data and academic submission, Denario provides a structured, reproducible environment that can either automate the entire research lifecycle or allow for manual intervention at any stage.

Key Features

Automated hypothesis generation from raw datasets
Computational experiment execution and data visualization
Publication-ready LaTeX paper generation for journals like APS
Interactive GUI for visual research workflow management
Multiagent orchestration for specialized scientific research tasks
89 GitHub stars

Use Cases

Accelerating literature reviews and hypothesis generation for new research projects
Automating the generation of complex research methodologies and experimental results
Converting research findings into professionally formatted LaTeX papers for journal submission

About

Key Features

Automated hypothesis generation from raw datasets
Computational experiment execution and data visualization
Publication-ready LaTeX paper generation for journals like APS
Interactive GUI for visual research workflow management
Multiagent orchestration for specialized scientific research tasks
89 GitHub stars

Use Cases

Accelerating literature reviews and hypothesis generation for new research projects
Automating the generation of complex research methodologies and experimental results
Converting research findings into professionally formatted LaTeX papers for journal submission