Multi-Agent AutoML + NLP Integration System

A Multi-Agent AI System that combines AutoML and NLP pipelines using collaborative agents. Each agent specializes in a specific task such as data preprocessing, feature engineering, model selection, hyperparameter optimization, NLP understanding, evaluation, and deployment—working together to deliver end-to-end automated ML and NLP solutions.

This project demonstrates agent collaboration, automation, and intelligent decision-making, aligned with modern AI system design.

---

✨ Key Features

• Multi-Agent Architecture: Independent agents with specialized responsibilities

• AutoML Pipeline:

  • Automated data preprocessing
  • Feature engineering
  • Model selection
  • Hyperparameter tuning

• NLP Integration:

  • Text preprocessing & tokenization
  • Embeddings & vectorization
  • NLP task handling (classification, summarization, Q&A)

• Agent Collaboration: Agents communicate and share intermediate results

• Adaptive Decision Making: Agents adjust strategies based on data & performance

• End-to-End Automation: From raw data to trained & evaluated models

• Scalable & Modular Design

---

🧠 System Architecture

User Input / Dataset
        ↓
Coordinator Agent
        ↓ assigns tasks
┌─────────────────────────────────────────┐
│ Specialized Agents                      │
│ • Data Preprocessing Agent              │
│ • Feature Engineering Agent             │
│ • NLP Processing Agent                  │
│ • AutoML / Model Selection Agent        │
│ • Hyperparameter Tuning Agent           │
│ • Evaluation Agent                      │
└─────────────────────────────────────────┘
        ↓
Shared Memory / Knowledge Store
        ↓
Final Model, Metrics & Insights

---

🛠 Tech Stack

• Language: Python 3.10+

• Agent Framework: LangGraph Custom Agent Orchestration

• AutoML: sklearn

• NLP:

  • spaCy / NLTK
  • Hugging Face Transformers
  • Sentence-Transformers

• ML Frameworks: Scikit-learn, XGBoost, LightGBM

• LLMs (Optional): Local LLMs

• Backend: FastAPI

• Visualization: Matplotlib, Seaborn

---

🧩 Agent Responsibilities

Agent	Responsibility
Coordinator Agent	Task planning & agent orchestration
Data Agent	Data cleaning, missing values, scaling
Feature Agent	Feature extraction & selection
NLP Agent	Text preprocessing & embeddings
AutoML Agent	Model search & selection
Tuning Agent	Hyperparameter optimization
Evaluation Agent	Metrics, validation & comparison

---

⚙️ Setup & Installation

git clone https://github.com/your-username/multi-agent-automl-nlp.git
cd multi-agent-automl-nlp
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

---

🚀 Running the System

using FastAPI:

uvicorn fastapp.main:app --reload

---

💬 Example Use Cases

• Automated text classification with model optimization
• NLP-driven AutoML for document analytics
• End-to-end ML model generation from raw datasets
• Rapid experimentation & benchmarking

---

🧪 Evaluation Metrics

• Accuracy / F1-score / ROC-AUC
• NLP task-specific metrics (BLEU, ROUGE)
• Model training time
• Agent coordination efficiency

---

🔮 Future Enhancements

• Multi-agent System
• RAG-enhanced NLP agent
• UI dashboard for pipeline monitoring

---

📜 License

MIT License

---

👩‍💻 Author

Vaishnavi Barolia AI / ML Engineer Research Intern Evostra Venturs

---

This project showcases advanced AI system design using Multi-Agent collaboration, AutoML, and NLP integration, suitable for AI Engineer, ML Engineer, and LLM Engineer roles.