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GroupChat Example

!!! abstract "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

!!! info "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

!!! tip "API Key Configuration" Set your API key in the .env file: bash 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

!!! example "Agent Configuration" Here's how to set up your agents with specific roles:

```python
# 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

!!! example "GroupChat Setup" Configure the GroupChat with your agents:

```python
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

!!! example "Execute the Chat" Start the conversation between agents:

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

Complete Example

!!! success "Full Implementation" Here's the complete code combined:

```python
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

!!! info "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

!!! tip "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

!!! warning "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

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