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