🔍 Multi-Agent Company Research System

An intelligent multi-agent system that researches companies using 5 specialized AI agents working in parallel. Built with LangGraph and Google Gemini, it generates comprehensive research reports with visualizations and interactive UI.

✨ Features

• 5 Specialized AI Agents - Profile, Leadership, Financial, Market, and Signals agents working in parallel
• Interactive Web UI - Streamlit-based interface with real-time agent monitoring
• Comprehensive Reports - HTML reports with charts, tables, and visualizations
• Multiple Report Types - Executive summary, detailed analysis, or investor reports
• High Confidence - Average 85%+ confidence scores with source tracking
• Flexible Architecture - Run all agents or select specific ones based on your needs

🏗️ Architecture

The system uses 5 specialized AI agents that work in parallel:

Research Agents

1. 🏢 Profile Agent - Business model, products, revenue streams
2. 👥 Leadership Agent - Founders, executives, management team
3. 💰 Financial Agent - Revenue, profitability, funding, financial ratios
4. 📊 Market Agent - Market size, competitors, SWOT analysis
5. 🚨 Signals Agent - News, sentiment, risks, hiring trends

Processing Pipeline

START → [5 Agents in Parallel] → Aggregate → Synthesize → Generate Report

Benefits:

• ⚡ 2.5x faster than sequential execution
• 🎯 85%+ average confidence scores
• 📊 45+ sources per company
• 📝 Comprehensive reports with visualizations

🚀 Installation

Prerequisites

• Python 3.9 or higher
• Google Gemini API key
• Tavily API key (for web search)

Step 1: Clone the Repository

git clone https://github.com/yourusername/company-researcher.git
cd company-researcher

Step 2: Create Virtual Environment

Windows:

python -m venv venv
venv\Scripts\activate

macOS/Linux:

python -m venv venv
source venv/bin/activate

Step 3: Install Dependencies

pip install -r requirements.txt

Or install as a package:

pip install -e .

Step 4: Configure API Keys

1. Copy the example environment file:

copy .env.example .env  # Windows
cp .env.example .env    # macOS/Linux

2. Edit .env and add your API keys:

# Required
GOOGLE_API_KEY=your_google_api_key_here
TAVILY_API_KEY=your_tavily_api_key_here

# Optional (for enhanced data)
ALPHA_VANTAGE_API_KEY=your_key_here
FMP_API_KEY=your_key_here
NEWS_API_KEY=your_key_here

Get API Keys:

• Google Gemini: https://ai.google.dev/
• Tavily: https://tavily.com/
• Alpha Vantage (optional): https://www.alphavantage.co/
• Financial Modeling Prep (optional): https://financialmodelingprep.com/

🎯 Usage

Option 1: Web UI (Recommended)

Launch the interactive Streamlit interface:

streamlit run ui.py

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

Features:

• Select which agents to run
• Choose report type (executive, detailed, investor)
• Real-time agent monitoring
• Interactive HTML reports with charts
• Export reports

Option 2: Python API

Use the system programmatically:

import asyncio
from src.graph.multi_agent_graph import research_company

async def main():
    # Research a company
    result = await research_company("Tesla")
    
    # Access results
    print(result['final_report'])
    print(f"Confidence: {result['report_metadata']['average_confidence']:.2%}")
    print(f"Sources: {len(result['all_sources'])}")

asyncio.run(main())

Option 3: Selective Agents

Run only specific agents:

from src.graph.selective_graph import research_selective

# Run only financial and market agents
result = await research_selective(
    company="Apple",
    selected_agents=["financial", "market"],
    report_type="executive"
)

Option 4: Command Line Testing

Test individual agents:

python test_intelligent_graph.py

Quick test:

python test_intelligent_graph_quick.py

📊 Report Types

Executive Summary

• High-level overview
• Key metrics and insights
• 1-2 page format
• Perfect for quick briefings

Detailed Analysis

• Comprehensive research
• All agent findings
• Charts and visualizations
• 5-10 page format

Investor Report

• Financial focus
• Market analysis
• Risk assessment
• Investment recommendations

📁 Project Structure

company-researcher/
├── src/
│   ├── agent/          # LangGraph agent configuration
│   ├── agents/         # Individual research agents
│   │   ├── intelligence/  # Intelligent routing
│   │   └── research/      # Research agents
│   ├── graph/          # Multi-agent orchestration
│   ├── llm/            # LLM configuration
│   ├── tools/          # Search and data tools
│   │   ├── analysis/   # Data analysis tools
│   │   ├── data/       # Financial data APIs
│   │   ├── search/     # Web search tools
│   │   └── visualization/  # Charts and tables
│   ├── ui/             # Streamlit web interface
│   └── voice/          # Voice interface (future)
├── docs/               # Documentation
├── eval/               # Evaluation scripts
├── test_reports/       # Generated reports
├── .env.example        # Environment template
├── requirements.txt    # Python dependencies
├── pyproject.toml      # Package configuration
└── ui.py              # Main UI entry point

🔧 Configuration

Adjust Research Depth

Edit src/graph/multi_agent_graph.py:

# More queries = more depth
max_queries=5,

# More results = more sources
max_results_per_query=3

Adjust Rate Limiting

rate_limiter = InMemoryRateLimiter(
    requests_per_second=0.15,  # Adjust for your API limits
    check_every_n_seconds=60,
    max_bucket_size=10
)

Change LLM Model

Edit src/llm/llm_config.py:

model = ChatGoogleGenerativeAI(
    model="gemini-2.0-flash",  # or "gemini-2.5-pro"
    temperature=0.7
)

📚 Documentation

• Setup Guide - Detailed installation instructions
• Quick Start - Get started quickly
• Architecture - System design and workflow
• Agents - Agent specifications and capabilities
• Tools - Available tools and integrations
• API Keys - API keys setup guide

🧪 Testing

Run the test suite:

# Test intelligent graph
python test_intelligent_graph.py

# Quick test
python test_intelligent_graph_quick.py

# Test report formatter
python test_report_formatter.py

📈 Performance

• Speed: 2.5x faster than sequential execution
• Confidence: 85%+ average across all agents
• Sources: 45+ unique sources per company
• Time: ~3-4 minutes per company (rate limited)

🛠️ Troubleshooting

Rate Limit Errors

If you see 429 You exceeded your current quota:

1. Wait 1 minute for rate limit to reset
2. Reduce requests_per_second in configuration
3. Consider upgrading your API plan

Low Quality Results

Increase research depth:

• Increase max_queries (more search queries)
• Increase max_results_per_query (more sources per query)
• Add optional API keys for better data

Import Errors

Make sure all dependencies are installed:

pip install -r requirements.txt

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

MIT License - see LICENSE file for details

🙏 Acknowledgments

• Built with LangGraph
• Powered by Google Gemini
• Search by Tavily

📧 Support

For issues and questions:

• Open an issue on GitHub
• Check the documentation
• Review existing issues for solutions

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Made with ❤️ using LangGraph and Google Gemini