🤖 Intelligent Research & Report Generator Agent

An autonomous AI agent that conducts comprehensive research on any topic and generates detailed, well-structured reports.

🎯 Problem Statement

Manual research is time-consuming, inconsistent, and often incomplete. This agent autonomously:

• Researches topics across multiple sources
• Synthesizes information intelligently
• Generates comprehensive, well-structured reports
• Adapts research depth based on query complexity

🚀 Features

• 🖥️ Interactive Web UI: Beautiful, modern interface with real-time progress tracking
• Autonomous Research: Automatically searches and gathers information from multiple sources
• Intelligent Synthesis: Combines findings from different sources into coherent insights
• Smart Report Generation: Creates well-formatted reports with citations and structure
• Multi-step Reasoning: Plans research strategy and executes systematically
• Source Tracking: Maintains references and citations for all information
• Multiple Interfaces: Choose between Web UI or CLI based on your preference

📁 Project Structure

├── src/
│   ├── agent/
│   │   ├── __init__.py
│   │   ├── research_agent.py      # Main agent orchestrator
│   │   └── planner.py             # Research planning logic
│   ├── modules/
│   │   ├── __init__.py
│   │   ├── web_researcher.py      # Web search and content extraction
│   │   ├── synthesizer.py         # Information synthesis
│   │   └── report_generator.py    # Report creation
│   └── utils/
│       ├── __init__.py
│       └── config.py              # Configuration management
├── outputs/                        # Generated reports
├── .env.example                    # Environment variables template
├── requirements.txt                # Python dependencies
├── app.py                          # Web UI (Streamlit)
├── main.py                         # CLI entry point
├── run_ui.bat                      # Windows launcher for UI
└── run_ui.sh                       # Linux/Mac launcher for UI

🛠️ Setup

1. Install dependencies:

pip install -r requirements.txt

2. Set up environment variables:

cp .env.example .env
# Edit .env and add your API keys:
# - GOOGLE_API_KEY (for Gemini)
# - TAVILY_API_KEY (for web search)

3. Run the application:

Option 1: Web UI (Recommended) 🌐

# Windows
run_ui.bat

# Linux/Mac
chmod +x run_ui.sh
./run_ui.sh

# Or directly:
streamlit run app.py

Then open your browser to http://localhost:8501

Option 2: Command Line Interface 💻

python main.py "Your research topic here"

# With options:
python main.py "Impact of AI on healthcare" --depth deep --format html

📝 Usage Examples

Web UI

1. Open the application in your browser
2. Enter your research topic
3. Select research depth (Auto/Shallow/Medium/Deep)
4. Click "Start Research"
5. View results and download reports

CLI

# Basic research
python main.py "Impact of AI on healthcare"

# Deep research with HTML output
python main.py "Climate change solutions" --depth deep --format html

# Quick research without saving
python main.py "Python best practices" --depth shallow --no-save

Python API

from src.agent.research_agent import ResearchAgent

agent = ResearchAgent()
result = agent.research("Impact of AI on healthcare", depth="medium")
print(result['report'])
print(f"Report saved to: {result['report_path']}")

🔧 Technologies

• LangChain: Agent framework and orchestration
• Google Gemini: LLM for reasoning and generation (free API available)
• Tavily/DuckDuckGo: Web search capabilities
• BeautifulSoup: Web content extraction
• Markdown: Report formatting