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GroupChat Example

Overview

Learn how to create and configure a group chat with multiple AI agents using the Swarms framework. This example demonstrates how to set up agents for expense analysis and budget advising.

Prerequisites

Before You Begin

Make sure you have: - Python 3.7+ installed - A valid API key for your model provider - The Swarms package installed

Installation

pip install swarms

Environment Setup

API Key Configuration

Set your API key in the .env file: 

OPENAI_API_KEY="your-api-key-here"

Code Implementation

Import Required Modules

from dotenv import load_dotenv
import os
from swarms import Agent, GroupChat

Configure Agents

Agent Configuration

Here's how to set up your agents with specific roles:

# Expense Analysis Agent
agent1 = Agent(
    agent_name="Expense-Analysis-Agent",
    description="You are an accounting agent specializing in analyzing potential expenses.",
    model_name="gpt-4o-mini",
    max_loops=1,
    autosave=False,
    dashboard=False,
    verbose=True,
    dynamic_temperature_enabled=True,
    user_name="swarms_corp",
    retry_attempts=1,
    context_length=200000,
    output_type="string",
    streaming_on=False,
    max_tokens=15000,
)

# Budget Adviser Agent
agent2 = Agent(
    agent_name="Budget-Adviser-Agent",
    description="You are a budget adviser who provides insights on managing and optimizing expenses.",
    model_name="gpt-4o-mini",
    max_loops=1,
    autosave=False,
    dashboard=False,
    verbose=True,
    dynamic_temperature_enabled=True,
    user_name="swarms_corp",
    retry_attempts=1,
    context_length=200000,
    output_type="string",
    streaming_on=False,
    max_tokens=15000,
)

Initialize GroupChat

GroupChat Setup

Configure the GroupChat with your agents:

agents = [agent1, agent2]

chat = GroupChat(
    name="Expense Advisory",
    description="Accounting group focused on discussing potential expenses",
    agents=agents,
    max_loops=1,
    output_type="all",
)

Run the Chat

Execute the Chat

Start the conversation between agents:

history = chat.run(
    "What potential expenses should we consider for the upcoming quarter? Please collaborate to outline a comprehensive list."
)

Complete Example

Full Implementation

Here's the complete code combined:

from dotenv import load_dotenv
import os
from swarms import Agent, GroupChat

if __name__ == "__main__":
    # Load environment variables
    load_dotenv()
    api_key = os.getenv("OPENAI_API_KEY")

    # Configure agents
    agent1 = Agent(
        agent_name="Expense-Analysis-Agent",
        description="You are an accounting agent specializing in analyzing potential expenses.",
        model_name="gpt-4o-mini",
        max_loops=1,
        autosave=False,
        dashboard=False,
        verbose=True,
        dynamic_temperature_enabled=True,
        user_name="swarms_corp",
        retry_attempts=1,
        context_length=200000,
        output_type="string",
        streaming_on=False,
        max_tokens=15000,
    )

    agent2 = Agent(
        agent_name="Budget-Adviser-Agent",
        description="You are a budget adviser who provides insights on managing and optimizing expenses.",
        model_name="gpt-4o-mini",
        max_loops=1,
        autosave=False,
        dashboard=False,
        verbose=True,
        dynamic_temperature_enabled=True,
        user_name="swarms_corp",
        retry_attempts=1,
        context_length=200000,
        output_type="string",
        streaming_on=False,
        max_tokens=15000,
    )

    # Initialize GroupChat
    agents = [agent1, agent2]
    chat = GroupChat(
        name="Expense Advisory",
        description="Accounting group focused on discussing potential expenses",
        agents=agents,
        max_loops=1,
        output_type="all",
    )

    # Run the chat
    history = chat.run(
        "What potential expenses should we consider for the upcoming quarter? Please collaborate to outline a comprehensive list."
    )

Configuration Options

Key Parameters

Parameter Description Default
max_loops Maximum number of conversation loops 1
autosave Enable automatic saving of chat history False
dashboard Enable dashboard visualization False
verbose Enable detailed logging True
dynamic_temperature_enabled Enable dynamic temperature adjustment True
retry_attempts Number of retry attempts for failed operations 1
context_length Maximum context length for the model 200000
max_tokens Maximum tokens for model output 15000

Next Steps

What to Try Next

  1. Experiment with different agent roles and descriptions
  2. Adjust the max_loops parameter to allow for longer conversations
  3. Enable the dashboard to visualize agent interactions
  4. Try different model configurations and parameters

Troubleshooting

Common Issues

  • Ensure your API key is correctly set in the .env file
  • Check that all required dependencies are installed
  • Verify that your model provider's API is accessible
  • Monitor the verbose output for detailed error messages

Additional Resources