AuraGen Documentation
Welcome to AuraGen, a sophisticated data generation engine designed to produce diverse, high-quality risky trajectories for Agentic Systems.
Overview
AuraGen operates in two distinct stages:
Generate Harmless Trajectories: Create clean, task-oriented agent action/response traces across many scenarios
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
# 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