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):

```bash
uv init
uv add "denario[app]"
```

Or using pip:

```bash
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:

```python
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:

```python
den.get_idea()
```

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

```python
den.set_idea("Custom research hypothesis")
```

### 3. Methodology Development

Develop the research methodology:

```python
den.get_method()
```

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

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

### 4. Results Generation

Execute computational experiments and generate analysis:

```python
den.get_results()
```

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

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

### 5. Paper Generation

Create a publication-ready LaTeX paper:

```python
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:

```bash
denario run
```

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

## Common Workflows

### End-to-End Research Pipeline

```python
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)

```python
# 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