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MCP Agent Bot Conversation

This project demonstrates a multi-agent conversation system where two language model agents (BotA and BotB) interact with each other, orchestrated by a driver script. The agents utilize a local LLM served by Ollama, and the communication framework potentially leverages the Model Context Protocol (MCP), although the current implementation uses direct interactions defined within the bot scripts.

Features

  • Multi-Agent Interaction: Simulates a conversation between two distinct AI bots (BotA and BotB).
  • Local LLM Powered: Uses a locally hosted language model via Ollama (configured for phi3 by default).
  • Dockerized Ollama: Includes a Docker setup to easily run the required Ollama server and automatically pull the specified model.
  • MCP Framework: Built using the mcp Python SDK for the bot servers.
  • Conversation Logging: Records the full conversation history with timestamps in src/conversation_log.txt.

Technology Stack

Project Structure

mcp-agent/
├── Dockerfile                  # Defines the Docker image for running Ollama
├── README.md                   # This file
├── docker-entrypoint-ollama.sh # Helper script run by Dockerfile to start Ollama & pull model
└── src/
    ├── .env                    # Optional file for environment variables (e.g., OPENAI_API_KEY, if used)
    ├── bot_a.py                # Runs the MCP server for Bot A (listens on port 8001)
    ├── bot_b.py                # Runs the MCP server for Bot B (listens on port 8002)
    ├── converse.py             # Driver script orchestrating the conversation
    ├── requirements.txt        # Python dependencies
    ├── conversation_log.txt    # Log file for conversations (created on run)
    └── venv/                   # Optional: Python virtual environment directory

Setup

  1. Clone the Repository: 
    git clone https://github.com/vishalmakwana111/mcp-agent.git
cd mcp-agent
  2. Install Docker: Ensure you have Docker installed and running. (Install Docker)
  3. Build the Ollama Docker Image: This image includes Ollama and automatically pulls the required model (phi3 by default) when started. 
    docker build -t ollama-server-with-model .
  4. Set up Python Environment: It's recommended to use a virtual environment for the Python scripts. 
    # Navigate to the source directory
cd src

# Create a virtual environment (if you haven't already)
python -m venv venv

# Activate the virtual environment
# On Windows (Powershell):
.\venv\Scripts\Activate.ps1
# On macOS/Linux:
# source venv/bin/activate

# Install Python dependencies
pip install -r requirements.txt

Running the Application

  1. Start the Ollama Docker Container: Run the Ollama server in the background. This command also maps the necessary port.

    # Run this from the project root directory (mcp-agent/)
docker run -d --rm --name ollama -p 11434:11434 ollama-server-with-model
    • You can check the logs to see the model being pulled: docker logs -f ollama (Press Ctrl+C to stop following). Wait for the "Model pull complete" message before proceeding.

  2. Start Bot A:

    • Open a new terminal.
    • Navigate to the src directory: cd path/to/mcp-agent/src
    • Activate the virtual environment (e.g., .\venv\Scripts\Activate.ps1).
    • Run the bot: python bot_a.py

  3. Start Bot B:

    • Open another new terminal.
    • Navigate to the src directory: cd path/to/mcp-agent/src
    • Activate the virtual environment.
    • Run the bot: python bot_b.py

  4. Run the Conversation Driver:

    • Open yet another new terminal.
    • Navigate to the src directory: cd path/to/mcp-agent/src
    • Activate the virtual environment.
    • Run the driver script: python converse.py

Configuration

  • LLM Model: The model used by the bots (phi3) is configured in bot_a.py, bot_b.py, and docker-entrypoint-ollama.sh. Change the model name in all three places if you want to use a different Ollama model.
  • Ollama URL: The bots connect to http://localhost:11434/v1. This is configured in bot_a.py and bot_b.py.
  • Bot Prompts: The system prompts defining the bots' personas are set within bot_a.py and bot_b.py.
  • Log File: Conversation output is saved to src/conversation_log.txt.
  • Conversation Length/Delay: The number of turns and the delay between turns are set in the for loop and asyncio.sleep call within src/converse.py.
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