Contributing to Agentic AI Course

Thank you for your interest in improving this course! We welcome contributions from students, researchers, and practitioners.

Ways to Contribute

Report Issues

Found a bug, typo, or error? Open an issue with:

  • Title: Brief description of the issue
  • Location: File path or page URL
  • Description: What’s wrong and (optionally) how to fix it
  • Labels: Add appropriate labels (bug, enhancement, documentation)

Suggest Improvements

Have ideas for new content, better explanations, or additional examples?

  1. Check existing issues to avoid duplicates
  2. Open a new issue with the enhancement label
  3. Describe your suggestion with as much detail as possible

Submit Pull Requests

Ready to contribute code or content directly?

Getting Started

Fork and Clone 

# Fork the repository on GitHub, then:
git clone https://github.com/YOUR-USERNAME/agentic-artificial-intelligence.git
cd agentic-artificial-intelligence

# Add upstream remote
git remote add upstream https://github.com/Digital-AI-Finance/agentic-artificial-intelligence.git

Set Up Development Environment 

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# For documentation site
cd docs
bundle install
bundle exec jekyll serve

Create a Branch 

# Sync with upstream
git fetch upstream
git checkout main
git merge upstream/main

# Create feature branch
git checkout -b feature/your-feature-name

Contribution Guidelines

Code Style

Python:

  • Follow PEP 8
  • Use type hints for function signatures
  • Maximum line length: 88 characters (Black formatter)
  • Add docstrings to all public functions
def calculate_similarity(text1: str, text2: str) -> float:
    """Calculate semantic similarity between two texts.

    Args:
        text1: First text for comparison
        text2: Second text for comparison

    Returns:
        Similarity score between 0 and 1
    """
    # Implementation
    pass

Notebooks:

  • Clear all outputs before committing
  • Add markdown cells explaining each section
  • Include expected output examples
  • Test with both OpenAI and Anthropic where applicable

Documentation Style

Markdown:

  • Use sentence case for headings
  • Add blank lines around code blocks
  • Link to related sections where relevant
  • Use tables for comparisons

LaTeX/Beamer:

  • 8pt font size
  • Maximum 4 bullet points per slide
  • No code on slides (reference notebooks instead)
  • Include bottomnotes for key takeaways

Commit Messages

Use conventional commit format:

type(scope): short description

Longer description if needed.

Closes #123

Types:

  • feat: New feature
  • fix: Bug fix
  • docs: Documentation only
  • style: Formatting, no code change
  • refactor: Code change that neither fixes nor adds
  • test: Adding tests
  • chore: Maintenance tasks

Examples:

feat(L05): add CrewAI multi-agent example

docs(glossary): add GraphRAG and HippoRAG terms

fix(L03): correct API key loading in notebook

Pull Request Process

  1. Before submitting:
    • Ensure your code runs without errors
    • Clear notebook outputs
    • Update relevant documentation
    • Add tests if applicable
  2. PR Description:
    • Reference the issue it addresses
    • Describe what changed and why
    • List any breaking changes
    • Include screenshots for UI changes
  3. Review process:
    • Address reviewer feedback promptly
    • Keep discussions focused on the code
    • Be patient - reviews may take a few days

Content Types

Adding a New Notebook

  1. Create in the appropriate week folder: LXX_Topic_Name/notebooks/
  2. Follow naming convention: LXX_notebook_name.ipynb
  3. Include Colab badge at the top:
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Digital-AI-Finance/agentic-artificial-intelligence/blob/main/LXX_Topic_Name/notebooks/LXX_notebook_name.ipynb)
  1. Structure sections with clear markdown headers
  2. Add error handling for API calls

Adding a Chart

  1. Create folder: LXX_Topic_Name/chart_name/
  2. Add chart.py that generates chart.pdf
  3. Use consistent color palette:
MLPURPLE = '#3333B2'
MLBLUE = '#0066CC'
MLORANGE = '#FF7F0E'
MLGREEN = '#2CA02C'
MLRED = '#D62728'
  1. figsize: (10, 6)
  2. Minimum font size: 10pt

Adding a Glossary Term

Edit docs/_data/glossary.yml:

- term: "Your Term"
  definition: "Clear, concise definition."
  related: ["Related Term 1", "Related Term 2"]
  week: 5

Adding a Paper

Edit docs/papers.md:

  1. Add to appropriate section
  2. Include full citation with DOI or arXiv link
  3. Verify link works

Quality Checklist

Before submitting, verify:

  • Code runs without errors
  • Notebook outputs cleared
  • No API keys or secrets committed
  • LaTeX compiles with zero overflow warnings
  • Links and references are valid
  • Spelling and grammar checked
  • Consistent with existing style

Accessibility Checklist

When adding content:

  • Images have alt text
  • Tables have header rows
  • Links are descriptive (not “click here”)
  • Code blocks have language specified
  • Color is not the only indicator

Recognition

Contributors are recognized in:

  • GitHub Contributors page
  • Course acknowledgments section
  • Release notes for significant contributions

Questions?

Thank you for helping improve this course for everyone!

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