Denario

Overview

Denario is a multiagent AI system designed to automate scientific research workflows from initial data analysis through publication-ready manuscripts. Built on AG2 and LangGraph frameworks, it orchestrates multiple specialized agents to handle hypothesis generation, methodology development, computational analysis, and paper writing.

When to Use This Skill

Use this skill when:

• Analyzing datasets to generate novel research hypotheses
• Developing structured research methodologies
• Executing computational experiments and generating visualizations
• Conducting literature searches for research context
• Writing journal-formatted LaTeX papers from research results
• Automating the complete research pipeline from data to publication

Installation

Install denario using uv (recommended):

uv init
uv add "denario[app]"

Or using pip:

uv pip install "denario[app]"

For Docker deployment or building from source, see references/installation.md.

LLM API Configuration

Denario requires API keys from supported LLM providers. Supported providers include:

• Google Vertex AI
• OpenAI
• Other LLM services compatible with AG2/LangGraph

Store API keys securely using environment variables or .env files. For detailed configuration instructions including Vertex AI setup, see references/llm_configuration.md.

Core Research Workflow

Denario follows a structured four-stage research pipeline:

1. Data Description

Define the research context by specifying available data and tools:

from denario import Denario

den = Denario(project_dir="./my_research")
den.set_data_description("""
Available datasets: time-series data on X and Y
Tools: pandas, sklearn, matplotlib
Research domain: [specify domain]
""")

2. Idea Generation

Generate research hypotheses from the data description:

den.get_idea()