RoundRobinSwarm: Practical Tutorial

A comprehensive guide to using the RoundRobinSwarm architecture for collaborative multi-agent discussions with randomized turn order.

Overview

The RoundRobinSwarm implements an AutoGen-style communication pattern where agents collaborate in randomized order across multiple discussion rounds. Each agent receives full conversation context and is encouraged to build upon others' contributions, creating rich collaborative refinement through varied interaction patterns.

Feature	Description
Randomized Turn Order	Agents shuffled each loop preventing predictable patterns
Full Context Awareness	Every agent sees complete conversation history
Collaborative Prompting	Built-in prompts encourage agents to acknowledge and extend others' work
Automatic Retry Logic	Exponential backoff retry mechanism for reliability
Flexible Output Formats	Support for "final", "dict", and "list" output types

When to Use RoundRobinSwarm

Best For: - Collaborative discussions requiring multiple perspectives - Iterative refinement through group deliberation - Tasks benefiting from varied interaction patterns - Situations where order dependency should be minimized

Not Ideal For: - Tasks requiring strict sequential processing - Simple queries with single-agent answers - Workflows with fixed dependencies between steps

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Installation

pip install -U swarms

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Basic Example

from swarms import Agent, RoundRobinSwarm

# Create specialized agents
researcher = Agent(
    agent_name="Research-Specialist",
    system_prompt="You research and gather factual information on topics.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

analyst = Agent(
    agent_name="Data-Analyst",
    system_prompt="You analyze data and identify patterns and insights.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

strategist = Agent(
    agent_name="Business-Strategist",
    system_prompt="You develop actionable strategies based on research and analysis.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

# Initialize swarm
swarm = RoundRobinSwarm(
    agents=[researcher, analyst, strategist],
    max_loops=2,
    output_type="final",
    verbose=True
)

# Run collaborative task
result = swarm.run(
    "Analyze market opportunities for AI-powered customer service solutions"
)

print(result)

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Key Parameters

Parameter	Type	Default	Description
agents	List[Agent]	Required	Agents participating in the swarm
max_loops	int	1	Number of discussion rounds
output_type	str	"final"	Output format: "final", "dict", "list"
verbose	bool	False	Enable detailed logging
callback	callable	None	Function called after each loop
max_retries	int	3	Maximum retry attempts with exponential backoff

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Advanced Examples

Example 1: Product Design Review

from swarms import Agent, RoundRobinSwarm

# Design team agents
agents = [
    Agent(
        agent_name="UX-Designer",
        system_prompt="You focus on user experience, usability, and user research.",
        model_name="gpt-4o-mini",
        max_loops=1,
    ),
    Agent(
        agent_name="Product-Manager",
        system_prompt="You balance business goals, user needs, and technical feasibility.",
        model_name="gpt-4o-mini",
        max_loops=1,
    ),
    Agent(
        agent_name="Engineer",
        system_prompt="You evaluate technical implementation, scalability, and maintainability.",
        model_name="gpt-4o-mini",
        max_loops=1,
    ),
    Agent(
        agent_name="Designer",
        system_prompt="You focus on visual design, brand consistency, and aesthetics.",
        model_name="gpt-4o-mini",
        max_loops=1,
    ),
]

swarm = RoundRobinSwarm(
    agents=agents,
    max_loops=3,  # 3 rounds for thorough review
    verbose=True
)

result = swarm.run(
    "Review the proposed dashboard redesign focusing on data visualization and user workflows"
)

Example 2: Strategic Planning

from swarms import Agent, RoundRobinSwarm

# Strategy team
agents = [
    Agent(
        agent_name="Market-Analyst",
        system_prompt="Analyze market trends, competition, and opportunities.",
        model_name="gpt-4o-mini",
        max_loops=1,
    ),
    Agent(
        agent_name="Financial-Planner",
        system_prompt="Evaluate financial implications, ROI, and resource allocation.",
        model_name="gpt-4o-mini",
        max_loops=1,
    ),
    Agent(
        agent_name="Operations-Lead",
        system_prompt="Assess operational feasibility, execution plans, and timelines.",
        model_name="gpt-4o-mini",
        max_loops=1,
    ),
]

swarm = RoundRobinSwarm(
    agents=agents,
    max_loops=2,
    output_type="dict"  # Get structured output
)

result = swarm.run(
    "Develop a go-to-market strategy for our new SaaS product targeting enterprise customers"
)

Example 3: With Callback Function

from swarms import Agent, RoundRobinSwarm

def progress_callback(loop_num, result):
    """Track progress after each loop"""
    print(f"\n[Loop {loop_num + 1} Complete]")
    print(f"Latest result preview: {result[:100]}...")

swarm = RoundRobinSwarm(
    agents=[agent1, agent2, agent3],
    max_loops=3,
    callback=progress_callback,
    verbose=True
)

result = swarm.run("Complex multi-round task...")

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Use Cases

Use Case 1: Research Synthesis

agents = [
    Agent(agent_name="Medical-Researcher", system_prompt="Clinical research expert", model_name="gpt-4o-mini", max_loops=1),
    Agent(agent_name="Data-Scientist", system_prompt="Statistical analysis expert", model_name="gpt-4o-mini", max_loops=1),
    Agent(agent_name="Literature-Reviewer", system_prompt="Research synthesis expert", model_name="gpt-4o-mini", max_loops=1),
]

swarm = RoundRobinSwarm(agents=agents, max_loops=2)
result = swarm.run("Synthesize current research on effectiveness of immunotherapy for melanoma")

Use Case 2: Code Review

agents = [
    Agent(agent_name="Security-Reviewer", system_prompt="Security and vulnerabilities expert", model_name="gpt-4o-mini", max_loops=1),
    Agent(agent_name="Performance-Reviewer", system_prompt="Performance optimization expert", model_name="gpt-4o-mini", max_loops=1),
    Agent(agent_name="Architecture-Reviewer", system_prompt="Code architecture and patterns expert", model_name="gpt-4o-mini", max_loops=1),
]

swarm = RoundRobinSwarm(agents=agents, max_loops=2)
result = swarm.run("Review the authentication middleware implementation in auth.py")

Use Case 3: Content Creation

agents = [
    Agent(agent_name="Researcher", system_prompt="Research and fact-checking specialist", model_name="gpt-4o-mini", max_loops=1),
    Agent(agent_name="Writer", system_prompt="Content writing and storytelling specialist", model_name="gpt-4o-mini", max_loops=1),
    Agent(agent_name="Editor", system_prompt="Editing, clarity, and refinement specialist", model_name="gpt-4o-mini", max_loops=1),
]

swarm = RoundRobinSwarm(agents=agents, max_loops=2)
result = swarm.run("Create a comprehensive guide on using AI for customer service automation")

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Best Practices

1. Agent Diversity: Create agents with truly distinct roles and perspectives
2. Loop Count: Use 2-3 loops for most tasks; more loops for complex discussions
3. Clear Roles: Give each agent a specific area of expertise
4. Verbose Mode: Enable during development to see interaction patterns
5. Callback Usage: Use callbacks for progress tracking in long-running tasks

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Common Patterns

Pattern 1: Multidisciplinary Team

# Create a well-rounded team
team = [
    Agent(agent_name="Technical", system_prompt="Technical expert..."),
    Agent(agent_name="Business", system_prompt="Business expert..."),
    Agent(agent_name="Creative", system_prompt="Creative expert..."),
    Agent(agent_name="User-Focused", system_prompt="User advocate..."),
]

swarm = RoundRobinSwarm(agents=team, max_loops=2)

Pattern 2: Iterative Refinement

# Higher loops for refinement
swarm = RoundRobinSwarm(
    agents=[expert1, expert2, expert3],
    max_loops=4,  # More rounds for deeper refinement
)

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Related Architectures

Architecture	When to Use Instead
SequentialWorkflow	When tasks must flow in fixed order
InteractiveGroupChat	For real-time interactive discussions
MajorityVoting	When you need consensus via voting
GroupChat	For expertise-based speaker selection

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Next Steps

• Explore RoundRobinSwarm Quickstart
• See GitHub Examples
• Learn about Group Communication Patterns