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GroupChat

Enables dynamic collaboration through chat-based interaction

Swarm Type: GroupChat

Overview

The GroupChat swarm type enables dynamic collaboration between agents through a chat-based interface, facilitating real-time information sharing and decision-making. Agents participate in a conversational workflow where they can build upon each other's contributions, debate ideas, and reach consensus through natural dialogue.

Key features:

Interactive Dialogue: Agents communicate through natural conversation
Dynamic Collaboration: Real-time information sharing and building upon ideas
Consensus Building: Agents can debate and reach decisions collectively
Flexible Participation: Agents can contribute when relevant to the discussion

Use Cases

Brainstorming and ideation sessions
Multi-perspective problem analysis
Collaborative decision-making processes
Creative content development

API Usage

Basic GroupChat Example

=== "Shell (curl)" bash curl -X POST "https://api.swarms.world/v1/swarm/completions" \ -H "x-api-key: $SWARMS_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "name": "Product Strategy Discussion", "description": "Collaborative chat to develop product strategy", "swarm_type": "GroupChat", "task": "Discuss and develop a go-to-market strategy for a new AI-powered productivity tool targeting small businesses", "agents": [ { "agent_name": "Product Manager", "description": "Leads product strategy and development", "system_prompt": "You are a senior product manager. Focus on product positioning, features, user needs, and market fit. Ask probing questions and build on others ideas.", "model_name": "gpt-4o", "max_loops": 3, }, { "agent_name": "Marketing Strategist", "description": "Develops marketing and positioning strategy", "system_prompt": "You are a marketing strategist. Focus on target audience, messaging, channels, and competitive positioning. Contribute marketing insights to the discussion.", "model_name": "gpt-4o", "max_loops": 3, }, { "agent_name": "Sales Director", "description": "Provides sales and customer perspective", "system_prompt": "You are a sales director with small business experience. Focus on pricing, sales process, customer objections, and market adoption. Share practical sales insights.", "model_name": "gpt-4o", "max_loops": 3, }, { "agent_name": "UX Researcher", "description": "Represents user experience and research insights", "system_prompt": "You are a UX researcher specializing in small business tools. Focus on user behavior, usability, adoption barriers, and design considerations.", "model_name": "gpt-4o", "max_loops": 3, } ], "max_loops": 3 }'

=== "Python (requests)" ```python import requests import json

API_BASE_URL = "https://api.swarms.world"
API_KEY = "your_api_key_here"

headers = {
    "x-api-key": API_KEY,
    "Content-Type": "application/json"
}

swarm_config = {
    "name": "Product Strategy Discussion",
    "description": "Collaborative chat to develop product strategy",
    "swarm_type": "GroupChat",
    "task": "Discuss and develop a go-to-market strategy for a new AI-powered productivity tool targeting small businesses",
    "agents": [
        {
            "agent_name": "Product Manager",
            "description": "Leads product strategy and development",
            "system_prompt": "You are a senior product manager. Focus on product positioning, features, user needs, and market fit. Ask probing questions and build on others ideas.",
            "model_name": "gpt-4o",
            "max_loops": 3,
        },
        {
            "agent_name": "Marketing Strategist", 
            "description": "Develops marketing and positioning strategy",
            "system_prompt": "You are a marketing strategist. Focus on target audience, messaging, channels, and competitive positioning. Contribute marketing insights to the discussion.",
            "model_name": "gpt-4o",
            "max_loops": 3,
        },
        {
            "agent_name": "Sales Director",
            "description": "Provides sales and customer perspective",
            "system_prompt": "You are a sales director with small business experience. Focus on pricing, sales process, customer objections, and market adoption. Share practical sales insights.",
            "model_name": "gpt-4o",
            "max_loops": 3,
        },
        {
            "agent_name": "UX Researcher",
            "description": "Represents user experience and research insights",
            "system_prompt": "You are a UX researcher specializing in small business tools. Focus on user behavior, usability, adoption barriers, and design considerations.",
            "model_name": "gpt-4o",
            "max_loops": 3,
        }
    ],
    "max_loops": 3
}

response = requests.post(
    f"{API_BASE_URL}/v1/swarm/completions",
    headers=headers,
    json=swarm_config
)

if response.status_code == 200:
    result = response.json()
    print("GroupChat swarm completed successfully!")
    print(f"Cost: ${result['metadata']['billing_info']['total_cost']}")
    print(f"Execution time: {result['metadata']['execution_time_seconds']} seconds")
    print(f"Chat discussion: {result['output']}")
else:
    print(f"Error: {response.status_code} - {response.text}")
```

Example Response:

{
    "job_id": "swarms-2COVtf3k0Fz7jU1BOOHF3b5nuL2x",
    "status": "success",
    "swarm_name": "Product Strategy Discussion",
    "description": "Collaborative chat to develop product strategy",
    "swarm_type": "GroupChat",
    "output": "User: \n\nSystem: \n Group Chat Name: Product Strategy Discussion\nGroup Chat Description: Collaborative chat to develop product strategy\n Agents in your Group Chat: Available Agents for Team: None\n\n\n\n[Agent 1]\nName: Product Manager\nDescription: Leads product strategy and development\nRole.....",
    "number_of_agents": 4,
    "service_tier": "standard",
    "execution_time": 47.36732482910156,
    "usage": {
        "input_tokens": 30,
        "output_tokens": 1633,
        "total_tokens": 1663,
        "billing_info": {
            "cost_breakdown": {
                "agent_cost": 0.04,
                "input_token_cost": 0.00009,
                "output_token_cost": 0.024495,
                "token_counts": {
                    "total_input_tokens": 30,
                    "total_output_tokens": 1633,
                    "total_tokens": 1663
                },
                "num_agents": 4,
                "service_tier": "standard",
                "night_time_discount_applied": false
            },
            "total_cost": 0.064585,
            "discount_active": false,
            "discount_type": "none",
            "discount_percentage": 0
        }
    }
}

Best Practices

Set clear discussion goals and objectives
Use diverse agent personalities for richer dialogue
Allow multiple conversation rounds for idea development
Encourage agents to build upon each other's contributions

MixtureOfAgents - For complementary expertise
MajorityVoting - For consensus decision-making
AutoSwarmBuilder - For automatic discussion setup

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