denario

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.

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

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.

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.

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.

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

Run the graphical user interface:

```bash

denario run

```

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

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.

• 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

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

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