Denario

概述

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.

使用時機

適用於以下情境：

• 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

安裝方式

安裝 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.

進階功能

• 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

疑難排解

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