K-Dense-AI/denario

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()

This produces a research question or hypothesis based on the described data. Alternatively, provide a custom idea:

den.set_idea("Custom research hypothesis")

3. Methodology Development

Develop the research methodology:

den.get_method()

This creates a structured approach for investigating the hypothesis. Can also accept markdown files with custom methodologies:

den.set_method("path/to/methodology.md")

4. Results Generation

Execute computational experiments and generate analysis:

den.get_results()

This runs the methodology, performs computations, creates visualizations, and produces findings. Can also provide pre-computed results:

den.set_results("path/to/results.md")

5. Paper Generation

Create a publication-ready LaTeX paper:

from denario import Journal

den.get_paper(journal=Journal.APS)

The generated paper includes proper formatting for the specified journal, integrated figures, and complete LaTeX source.

Available Journals

Denario supports multiple journal formatting styles:

• Journal.APS - American Physical Society format
• Additional journals may be available; check references/research_pipeline.md for the complete list

Launching the GUI

Run the graphical user interface:

denario run

This launches a web-based interface for interactive research workflow management.

Common Workflows

End-to-End Research Pipeline

from denario import Denario, Journal

# Initialize project
den = Denario(project_dir="./research_project")

# Define research context
den.set_data_description("""
Dataset: Time-series measurements of [phenomenon]
Available tools: pandas, sklearn, scipy
Research goal: Investigate [research question]
""")

# Generate research idea
den.get_idea()

# Develop methodology
den.get_method()

# Execute analysis
den.get_results()

# Create publication
den.get_paper(journal=Journal.APS)

Hybrid Workflow (Custom + Automated)

# Provide custom research idea
den.set_idea("Investigate the correlation between X and Y using time-series analysis")

# Auto-generate methodology
den.get_method()

# Auto-generate results
den.get_results()

# Generate paper
den.get_paper(journal=Journal.APS)

Literature Search Integration

For literature search functionality and additional workflow examples, see references/examples.md.

Advanced Features

• Multiagent orchestration: AG2 and LangGraph coordinate specialized agents for different research tasks
• Reproducible research: All stages produce structured outputs that can be version-controlled
• Journal integration: Automatic formatting for target publication venues
• Flexible input: Manual or automated at each pipeline stage
• Docker deployment: Containerized environment with LaTeX and all dependencies

Detailed References

For comprehensive documentation:

• Installation options: references/installation.md
• LLM configuration: references/llm_configuration.md
• Complete API reference: references/research_pipeline.md
• Example workflows: references/examples.md

Troubleshooting

Common issues and solutions:

• API key errors: Ensure environment variables are set correctly (see references/llm_configuration.md)
• LaTeX compilation: Install TeX distribution or use Docker image with pre-installed LaTeX
• Package conflicts: Use virtual environments or Docker for isolation
• Python version: Requires Python 3.12 or higher