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## Interactive Groupchat Examples
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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.
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### Architecture Description
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The Interactive GroupChat implements a **collaborative swarm architecture** where multiple specialized agents work together in a coordinated manner. Key features include:
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- **Mention-based Communication**: Agents can be directed to specific tasks using `@agent_name` syntax
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- **Flexible Speaker Functions**: Multiple speaking order strategies (round robin, random, priority-based)
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- **Enhanced Collaboration**: Agents build upon each other's responses and avoid redundancy
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- **Interactive Sessions**: Support for both automated and interactive conversation modes
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- **Context Awareness**: Agents maintain conversation history and context
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For comprehensive documentation on Interactive GroupChat, visit: [Interactive GroupChat Documentation](https://docs.swarms.world/en/latest/swarms/structs/interactive_groupchat/)
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### Step-by-Step Showcase
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* **Agent Creation**: Define specialized agents with unique expertise and system prompts
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* **GroupChat Initialization**: Create the InteractiveGroupChat structure with desired speaker function
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* **Task Definition**: Formulate tasks using `@agent_name` mentions to direct specific agents
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* **Execution**: Run the group chat to generate collaborative responses
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* **Response Processing**: Handle the coordinated output from multiple agents
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* **Iteration**: Chain multiple tasks for complex workflows
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## Installation
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Install the swarms package using pip:
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```bash
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pip install -U swarms
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```
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## Basic Setup
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1. First, set up your environment variables:
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```python
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WORKSPACE_DIR="agent_workspace"
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OPENAI_API_KEY=""
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```
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## Code
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```python
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"""
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InteractiveGroupChat Speaker Function Examples
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This example demonstrates how to use different speaker functions in the InteractiveGroupChat:
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- Round Robin: Agents speak in a fixed order, cycling through the list
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- Random: Agents speak in random order
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- Priority: Agents speak based on priority weights
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- Custom: User-defined speaker functions
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The example also shows how agents can mention each other using @agent_name syntax.
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"""
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from swarms import Agent
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from swarms.structs.interactive_groupchat import (
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InteractiveGroupChat,
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random_speaker,
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)
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def create_example_agents():
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"""Create example agents for demonstration."""
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# Create agents with different expertise
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analyst = Agent(
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agent_name="analyst",
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system_prompt="You are a data analyst. You excel at analyzing data, creating charts, and providing insights.",
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model_name="gpt-4.1",
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streaming_on=True,
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print_on=True,
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)
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researcher = Agent(
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agent_name="researcher",
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system_prompt="You are a research specialist. You are great at gathering information, fact-checking, and providing detailed research.",
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model_name="gpt-4.1",
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streaming_on=True,
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print_on=True,
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)
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writer = Agent(
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agent_name="writer",
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system_prompt="You are a content writer. You excel at writing clear, engaging content and summarizing information.",
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model_name="gpt-4.1",
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streaming_on=True,
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print_on=True,
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)
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return [analyst, researcher, writer]
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def example_random():
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agents = create_example_agents()
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# Create group chat with random speaker function
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group_chat = InteractiveGroupChat(
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name="Random Team",
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description="A team that speaks in random order",
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agents=agents,
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speaker_function=random_speaker,
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interactive=False,
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)
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# Test the random behavior
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task = "Let's create a marketing strategy. @analyst @researcher @writer please contribute."
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response = group_chat.run(task)
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print(f"Response:\n{response}\n")
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if __name__ == "__main__":
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# example_round_robin()
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example_random()
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```
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## Connect With Us
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Join our community of agent engineers and researchers for technical support, cutting-edge updates, and exclusive access to world-class agent engineering insights!
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| Platform | Description | Link |
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|----------|-------------|------|
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| 📚 Documentation | Official documentation and guides | [docs.swarms.world](https://docs.swarms.world) |
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| 📝 Blog | Latest updates and technical articles | [Medium](https://medium.com/@kyeg) |
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| 💬 Discord | Live chat and community support | [Join Discord](https://discord.gg/jM3Z6M9uMq) |
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| 🐦 Twitter | Latest news and announcements | [@kyegomez](https://twitter.com/kyegomez) |
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| 👥 LinkedIn | Professional network and updates | [The Swarm Corporation](https://www.linkedin.com/company/the-swarm-corporation) |
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| 📺 YouTube | Tutorials and demos | [Swarms Channel](https://www.youtube.com/channel/UC9yXyitkbU_WSy7bd_41SqQ) |
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| 🎫 Events | Join our community events | [Sign up here](https://lu.ma/5p2jnc2v) |
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| 🚀 Onboarding Session | Get onboarded with Kye Gomez, creator and lead maintainer of Swarms | [Book Session](https://cal.com/swarms/swarms-onboarding-session) |
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@ -1,13 +0,0 @@
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from swarms import Agent
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# Initialize a new agent
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agent = Agent(
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model_name="gpt-4o-mini", # Specify the LLM
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max_loops=1, # Set the number of interactions
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interactive=True, # Enable interactive mode for real-time feedback
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streaming_on=True,
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print_on=False,
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)
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# Run the agent with a task
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agent.run("What are the key benefits of using a multi-agent system?")
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