AD-AGENT

AD-AGENT is a multi-agent anomaly detection platform designed to support the full lifecycle of real-world anomaly detection—from data preprocessing and model selection to detection, explanation, and evaluation. It integrates classical and graph-based AD algorithms with LLM-powered modules for enhanced usability, privacy, and adaptability.

🔍 One platform. Multiple agents. All your anomaly detection workflows—automated, explainable, and secure.

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🔧 Features

• Modular Pipeline Execution: Supports multivariate, graph, and time-series anomaly detection algorithms for more and more complex data types.
• Multi-Agent Architecture: Detection, explanation, and adaptation are handled by decoupled agents with clear APIs and extendability.
• LLM Integration (in progress): Language models assist in tasks such as explanation, synthetic anomaly generation, and interactive debugging.
• Privacy-Aware Design (in progress): Includes a framework for anonymizing data before AD processing, suitable for regulated domains.
• Human-in-the-loop Support (in progress): Enables analysts to query explanations and iterate on detection results interactively.

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🔧 Setup Instructions

1. Clone the Repository

git clone [email protected]:USC-FORTIS/AD-AGENT.git
cd AD-AGENT

2. Create and Activate a Virtual Environment

On macOS/Linux:

python -m venv .venv
source .venv/bin/activate

On Windows:

python -m venv .venv
.venv\Scripts\activate

3. Install Dependencies

pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.5.0+cpu.html

pip install -r requirements.txt

4. Set Your OpenAI API Key

Edit the config file to include your OpenAI API key:

# File: /config/config.py

OPENAI_API_KEY = 'your-api-key-here'

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🚀 Running the Program

Run Normally (Sequential Execution)

python main.py

Run in Parallel Mode

python main.py -p

Run in Optimizer Mode

python main.py -o

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🧪 Test Commands

You can also run the system with natural-language-like test commands.

Run a Specific Algorithm

# PyOD
Run IForest on ./data/glass_train.mat and ./data/glass_test.mat
Run all on ./data/glass_train.mat and ./data/glass_test.mat
# PyGOD
Run DOMINANT on ./data/inj_cora_train.pt and ./data/inj_cora_test.pt
# TSLib 
Run LightTS on ./data/MSL and ./data/MSL
# Darts (in progress)
Run GlobalNaiveAggregate on ./data/yahoo_train.csv and ./data/yahoo_test.csv

Run All Algorithms

Run all on ./data/glass_train.mat and ./data/glass_test.mat

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📁 Project Structure

.
├── config/
│   └── config.py             # Configuration file for API keys
.
.
.
├── data/
│   └── glass.mat             # Sample dataset
├── main.py                   # Main execution script
├── requirements.txt          # Required Python packages
└── README.md                 # Project documentation

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📌 Notes

• Make sure your dataset is placed inside the ./data/ directory.
• Modify main.py to add support for additional algorithms or datasets if needed.

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👥 Contributors

Made with contrib.rocks.