Agentic Research Assistant

A powerful AI-powered research automation system featuring a multi-agent architecture for comprehensive academic research and paper generation.

Features

Multi-Agent Architecture

• Research Director: Strategic planning and coordination
• Literature Agent: Advanced literature search and analysis
• Data Agent: Comprehensive data collection and insights
• Writer Agent: Automated research paper generation
• Editor Agent: Quality assurance and final editing

Research Capabilities

• Automated literature review
• Data analysis and insights
• Academic paper generation
• Citation management
• Multiple export formats

Project Structure

agentic-research-assistant/
├── src/
│   ├── core/
│   │   └── assistant.py               # Core research logic
│   └── ui/
│       └── interface.py               # Agentic Streamlit UI
├── main.py                            # Main launcher script
├── requirements.txt                    # Python dependencies
├── pyproject.toml                     # Project configuration
├── config.py                          # Application configuration
├── .python-version                    # Python version specification
└── README.md                          # This file

Quick Start

1. Clone the Repository

git clone https://github.com/thillai-c/agentic-research-assistant.git
cd agentic-research-assistant

2. Install Dependencies

pip install -r requirements.txt

3. Set Up Environment Variables

Create a .env file in the project root:

GROQ_API_KEY=your_groq_api_key_here
TAVILY_API_KEY=your_tavily_api_key_here

# Optional: Research Configuration
RESEARCH_DEPTH=comprehensive
MAX_SOURCES=15
PAPER_TARGET_LENGTH=5000

Get API Keys:

• GROQ: https://console.groq.com/
• TAVILY: https://tavily.com/

4. Launch the Application

python main.py

The application will open in your browser at http://localhost:8501

Advanced Configuration

Research Parameters

• Research Scope: Comprehensive, Focused, Quick Review, Deep Analysis
• Target Audience: Academic Researchers, Students, Professionals, General Public
• Research Type: Comprehensive, Literature Review, Data Analysis, Case Study
• Paper Length: 1,000 - 15,000 words (configurable)

Agent Configuration

• Max Sources: Number of literature sources to analyze
• Min Relevance Score: Minimum relevance threshold for sources
• Enable Citations: Automatic citation generation
• Enable Plagiarism Check: Content originality verification

Usage Guide

1. Start Research

1. Enter your research topic in the sidebar
2. Select research scope and audience
3. Choose research type
4. Click "Start Agentic Research"

2. Monitor Progress

• Watch real-time progress updates
• View agent activity and status
• Track completion percentage
• Monitor quality metrics

3. Review Results

• Literature sources with relevance scores
• Data insights and analysis
• Paper draft and final version
• Quality metrics and performance data

4. Export Results

• JSON: Complete research data
• TXT: Final research paper
• Summary: Research overview
• Metrics: Performance analytics

User Interface

Development

Prerequisites

• Python 3.13+
• pip package manager
• Git version control

Local Development Setup

# Clone repository
git clone https://github.com/thillai-c/agentic-research-assistant.git
cd agentic-research-assistant

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Launch development server
python main.py

Code Structure

• Core Logic: src/core/assistant.py
• UI Components: src/ui/interface.py
• Configuration: pyproject.toml, requirements.txt
• Launch Scripts: main.py
• Settings: config.py

Performance Metrics

The system tracks various performance indicators:

• Completion Time: Total research duration
• Quality Score: Overall research quality (0-10)
• Source Relevance: Average source relevance score
• Data Insights: Number of data points analyzed
• Paper Length: Generated content length

Security & Privacy

• API keys stored in environment variables
• No data sent to external services without consent
• Local processing for sensitive research topics
• Configurable privacy settings

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

Common Issues

• API Key Errors: Ensure your .env file contains valid API keys
• Dependency Issues: Run pip install -r requirements.txt
• Port Conflicts: Change port in launcher script if 8501 is busy

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Developed by ThillaiC