Investment Committee Multi-Agent System

A multi-agent system built with LangGraph that simulates an investment committee debate between bullish and bearish perspectives, with a chairman making final investment decisions.

🎯 Overview

This system demonstrates true agentic behavior where three AI agents debate and make investment decisions:

• 🐂 Bull Agent: Finds positive reasons to buy stocks
• 🐻 Bear Agent: Finds negative reasons to avoid stocks
• 🎯 Chairman Agent: Makes final investment decisions after hearing both sides

🚀 Features

• Real-time web research using Tavily search
• Agentic debate where agents counter each other's arguments
• Multi-round discussion with structured workflow
• Final decision making with reasoning
• Interactive command-line interface

📁 Project Structure

investment-committee/
├── src/
│   ├── __init__.py          # Package marker
│   ├── config.py            # Prompts and model configuration
│   ├── tools.py             # Agent tools organized by function
│   ├── utils.py             # Utility functions for display
│   └── agents.py            # Agent and supervisor creation
├── main.py                  # Command-line interface
├── requirements.txt         # Python dependencies
├── .env.example             # Environment variables template
└── README.md                # This file

🛠️ Installation

1. Clone and navigate to the directory

   cd investment-committee

2. Install dependencies

   Install dependencies using uv, a fast Python package installer and resolver:

   uv sync

3. Set up environment variables

   Next, create an .env file and populate it with your API keys from OpenAI and Tavily.

   cp .env.example .env
   # Edit .env with your API keys

4. Run the application

   Run the application with the python command:

   python main.py

   or with uv:

   uv run main.py

💡 Usage

1. Start the application with python main.py
2. Enter any stock symbol (e.g., NVDA, TSLA, AAPL)
3. Watch the agents debate the investment merits
4. See the chairman's final decision with reasoning
5. Type 'quit' to exit

🔧 Configuration

• Model settings: Modify src/config.py
• Agent prompts: Customize prompts in src/config.py
• Tool behavior: Adjust tools in src/tools.py

📋 Requirements

• Python 3.8+
• OpenAI API key
• Tavily API key (for web search)

🎓 Educational Purpose

This project demonstrates:

• Multi-agent system architecture with LangGraph
• Agent-to-agent communication and debate
• Real-time web search integration
• Structured decision-making workflows
• Command-line interface design

Perfect for learning how to build collaborative AI systems!