What are the technical requirements for running Denario?

It requires Python 3.12 or higher. For full paper compilation, a local TeX distribution or the provided Docker image is recommended to handle LaTeX dependencies.

What LLM providers are supported by Denario?

Denario is compatible with LLM providers supported by AG2 and LangGraph, including Google Vertex AI, OpenAI, and other standard API services.

What is Denario's main purpose?

Denario is a multiagent AI system that automates the scientific research process, including hypothesis generation, methodology development, experiment execution, and paper writing.

Does Denario support journal-specific formatting?

Yes, it currently supports formats like APS (American Physical Society) and allows for the generation of complete, publication-ready LaTeX documents with integrated figures.

Can I use my own research ideas instead of AI-generated ones?

Absolutely. Denario allows a hybrid workflow where you can provide custom ideas or methodologies while automating the execution and writing stages of the pipeline.

Denario AI Scientist

Name: Denario AI Scientist
Author: jimmc414

byjimmc414

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324

Data Science & ML

Automates the end-to-end scientific research lifecycle from data analysis and hypothesis generation to publishing LaTeX-formatted papers.

About

Denario is a sophisticated multiagent AI system designed to streamline scientific discovery by orchestrating specialized agents for every stage of the research pipeline. It leverages AG2 and LangGraph to transform raw datasets into structured research ideas, rigorous methodologies, and executed computational experiments. Whether you're a researcher looking to accelerate literature reviews or a scientist needing to generate publication-ready manuscripts in journals like APS, Denario provides an autonomous workflow that handles the heavy lifting of data-driven discovery and experimental analysis.

Key Features

Multiagent orchestration for specialized research tasks including hypothesis and methodology development.
Journal-specific formatting support for major publications like the American Physical Society (APS).
End-to-end automation from raw data ingestion to publication-ready LaTeX papers.
324 GitHub stars
Reproducible research pipelines with structured, version-controllable outputs.
Autonomous execution of computational experiments and data visualization.

Use Cases

Automating the generation of scientific manuscripts from raw experimental datasets.
Standardizing research methodologies and documentation for academic lab environments.
Rapidly exploring and testing new research hypotheses based on existing data trends.

About

Key Features

Multiagent orchestration for specialized research tasks including hypothesis and methodology development.
Journal-specific formatting support for major publications like the American Physical Society (APS).
End-to-end automation from raw data ingestion to publication-ready LaTeX papers.
324 GitHub stars
Reproducible research pipelines with structured, version-controllable outputs.
Autonomous execution of computational experiments and data visualization.

Use Cases

Automating the generation of scientific manuscripts from raw experimental datasets.
Standardizing research methodologies and documentation for academic lab environments.
Rapidly exploring and testing new research hypotheses based on existing data trends.