Interactive GroupChat Example

Interactive Groupchat Examples

The Interactive GroupChat is a powerful multi-agent architecture that enables dynamic collaboration between multiple AI agents. This architecture allows agents to communicate with each other, respond to mentions using @agent_name syntax, and work together to solve complex tasks through structured conversation flows.

Architecture Description

The Interactive GroupChat implements a collaborative swarm architecture where multiple specialized agents work together in a coordinated manner. Key features include:

• Mention-based Communication: Agents can be directed to specific tasks using @agent_name syntax
• Flexible Speaker Functions: Multiple speaking order strategies (round robin, random, priority-based)
• Enhanced Collaboration: Agents build upon each other's responses and avoid redundancy
• Interactive Sessions: Support for both automated and interactive conversation modes
• Context Awareness: Agents maintain conversation history and context

For comprehensive documentation on Interactive GroupChat, visit: Interactive GroupChat Documentation

Step-by-Step Showcase

• Agent Creation: Define specialized agents with unique expertise and system prompts
• GroupChat Initialization: Create the InteractiveGroupChat structure with desired speaker function
• Task Definition: Formulate tasks using @agent_name mentions to direct specific agents
• Execution: Run the group chat to generate collaborative responses
• Response Processing: Handle the coordinated output from multiple agents
• Iteration: Chain multiple tasks for complex workflows

Installation

Install the swarms package using pip:

pip install -U swarms

Basic Setup

1. First, set up your environment variables:

WORKSPACE_DIR="agent_workspace"
OPENAI_API_KEY=""

Code

"""
InteractiveGroupChat Speaker Function Examples

This example demonstrates how to use different speaker functions in the InteractiveGroupChat:
- Round Robin: Agents speak in a fixed order, cycling through the list
- Random: Agents speak in random order
- Priority: Agents speak based on priority weights
- Custom: User-defined speaker functions

The example also shows how agents can mention each other using @agent_name syntax.
"""

from swarms import Agent
from swarms.structs.interactive_groupchat import (
    InteractiveGroupChat,
    random_speaker,
)


def create_example_agents():
    """Create example agents for demonstration."""

    # Create agents with different expertise
    analyst = Agent(
        agent_name="analyst",
        system_prompt="You are a data analyst. You excel at analyzing data, creating charts, and providing insights.",
        model_name="gpt-4.1",
        streaming_on=True,
        print_on=True,
    )

    researcher = Agent(
        agent_name="researcher",
        system_prompt="You are a research specialist. You are great at gathering information, fact-checking, and providing detailed research.",
        model_name="gpt-4.1",
        streaming_on=True,
        print_on=True,
    )

    writer = Agent(
        agent_name="writer",
        system_prompt="You are a content writer. You excel at writing clear, engaging content and summarizing information.",
        model_name="gpt-4.1",
        streaming_on=True,
        print_on=True,
    )

    return [analyst, researcher, writer]


def example_random():
    agents = create_example_agents()

    # Create group chat with random speaker function
    group_chat = InteractiveGroupChat(
        name="Random Team",
        description="A team that speaks in random order",
        agents=agents,
        speaker_function=random_speaker,
        interactive=False,
    )

    # Test the random behavior
    task = "Let's create a marketing strategy. @analyst @researcher @writer please contribute."

    response = group_chat.run(task)
    print(f"Response:\n{response}\n")


if __name__ == "__main__":
    # example_round_robin()
    example_random()

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