fulloch/CONTRIBUTING.md

Contributing to Fulloch

Thank you for your interest in contributing to Fulloch! This document provides guidelines for contributing to the project.

Getting Started

1. Fork the repository
2. Clone your fork locally
3. Set up the development environment:

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   pip install -r requirements.txt
   pip install -e ".[dev]"  # Install dev dependencies
   

4. Copy configuration files:

   cp data/config.example.yml data/config.yml
   cp .env.example .env
   

5. Edit data/config.yml with your settings

Adding New Tools

Fulloch uses a decorator-based tool registry system. To add a new tool:

Step 1: Create a new tool file

Create tools/my_tool.py:

"""
My new tool description.
"""
import yaml

with open("./data/config.yml", "r") as f:
    config = yaml.safe_load(f)

from .tool_registry import tool, tool_registry

# Load configuration if needed
MY_CONFIG = config.get('my_tool', {})


@tool(
    name="my_function",
    description="What this function does (shown to AI)",
    aliases=["alias1", "alias2"]  # Optional alternative names
)
def my_function(param1: str, param2: int = 10) -> str:
    """
    Detailed docstring for the function.

    Args:
        param1: Description of param1
        param2: Description of param2 (default: 10)

    Returns:
        Result message
    """
    # Implementation here
    return f"Result: {param1}, {param2}"

Step 2: Register the tool

Add the import to tools/__init__.py:

from . import my_tool

Add to the __all__ list:

__all__ = [
    # ... existing tools ...
    'my_tool',
]

Step 3: Add configuration (if needed)

Add a section to data/config.example.yml:

# =============================================================================
# My Tool
# =============================================================================
my_tool:
  setting1: "value1"
  setting2: 123

Step 4: Test your tool

python tools/my_tool.py

Code Style Guidelines

• Follow PEP 8 style guidelines
• Use type hints for function parameters and return values
• Write docstrings for all public functions and classes
• Keep functions focused and single-purpose
• Use meaningful variable and function names

Logging

Use the standard logging module:

import logging
logger = logging.getLogger(__name__)

logger.debug("Debug message")
logger.info("Info message")
logger.warning("Warning message")
logger.error("Error message")

Async Functions

For I/O-bound operations (network, file system), use async:

import asyncio

async def _my_async_function():
    """Internal async implementation."""
    # async code here
    pass


@tool(name="my_function", description="...")
def my_function():
    """Sync wrapper for the async function."""
    try:
        loop = asyncio.get_running_loop()
    except RuntimeError:
        return asyncio.run(_my_async_function())
    else:
        return loop.create_task(_my_async_function())

Pull Request Process

1. Create a feature branch from main:

   git checkout -b feature/my-feature
   

2. Make your changes and commit with clear messages:

   git commit -m "Add my new feature"
   

3. Run tests before submitting:

   pytest tests/
   

4. Push to your fork and create a Pull Request

5. Fill out the PR template with:

   • Summary of changes
   • Test plan
   • Any breaking changes

Reporting Issues

When reporting issues, please include:

• Python version (python --version)
• Operating system
• Steps to reproduce
• Expected vs actual behavior
• Relevant log output

Questions?

Feel free to open an issue for questions or discussion about potential contributions.