AuraGen Documentation
=====================

Welcome to AuraGen, a sophisticated data generation engine designed to produce diverse, high-quality risky trajectories for Agentic Systems.

.. image:: https://img.shields.io/badge/Python-3.8%2B-blue
   :alt: Python Version
   :target: https://python.org

.. image:: https://img.shields.io/badge/License-MIT-green.svg
   :alt: License
   :target: https://opensource.org/licenses/MIT

Overview
--------

AuraGen operates in two distinct stages:

1. **Generate Harmless Trajectories**: Create clean, task-oriented agent action/response traces across many scenarios
2. **Inject Risk**: Programmatically mutate the harmless trajectories to introduce realistic risks while maintaining coherence and plausibility

Key Features
------------

- Flexible API Integration (OpenAI, external APIs, custom providers)
- Dynamic Configuration via YAML (including custom API key types)
- Comprehensive Risk Injection Framework
- Scenario-based Design with Constraints

Quick Start
-----------

.. code-block:: bash

   # Clone the repository
   git clone <repository-url>
   cd Agentic-Guardian

   conda create -n aura python=3.11 -y
   conda activate aura

   # Install dependencies
   pip install -r requirements.txt --upgrade

   # Configure API keys
   python config/configure_api_keys.py

   # Generate and inject risks
   python generate_and_inject.py

Table of Contents
-----------------

.. toctree::
   :maxdepth: 2
   :caption: User Guide

   installation
   quickstart
   configuration
   scenarios
   risk_injection

.. toctree::
   :maxdepth: 2
   :caption: Advanced Topics

   advanced/custom_scenarios
   advanced/api_integration
   advanced/extending
   advanced/troubleshooting

.. toctree::
   :maxdepth: 1
   :caption: Development

   contributing
   changelog
   license

Indices and Tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`