Skip to content

RoundRobinSwarm: 3-Step Quickstart Guide

The RoundRobinSwarm implements an AutoGen-style communication pattern where agents are shuffled randomly each loop for varied interaction patterns. Each agent receives the full conversation context to build upon others' responses, creating collaborative and iterative refinement.

Overview

Feature Description
Randomized Order Agents are shuffled each loop for varied interaction patterns
Full Context Sharing Each agent sees complete conversation history
Collaborative Prompting Agents build on each other's contributions
Retry Mechanism Automatic retries with exponential backoff for reliability 
Loop 1: Agent B → Agent C → Agent A
Loop 2: Agent A → Agent B → Agent C  (shuffled)
Loop 3: Agent C → Agent A → Agent B  (shuffled)

Each agent sees full conversation history

Step 1: Install and Import

pip install swarms

from swarms import Agent, RoundRobinSwarm

Step 2: Create Agents and Swarm

# Create specialized agents
researcher = Agent(
    agent_name="Researcher",
    system_prompt="You are a research specialist. Gather and present factual information.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

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

writer = Agent(
    agent_name="Writer",
    system_prompt="You synthesize information into clear, actionable insights.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

# Create the swarm
swarm = RoundRobinSwarm(
    agents=[researcher, analyst, writer],
    max_loops=2,  # 2 rounds of discussion
    verbose=True
)

Step 3: Run the Swarm

# Execute the task
result = swarm.run(
    task="Analyze the impact of remote work on productivity and team collaboration"
)

print(result)

Complete Example

from swarms import Agent, RoundRobinSwarm

# Create diverse agents
agents = [
    Agent(
        agent_name="Tech-Expert",
        system_prompt="Technology and implementation specialist",
        model_name="gpt-4o-mini",
        max_loops=1,
    ),
    Agent(
        agent_name="Business-Strategist",
        system_prompt="Business strategy and ROI specialist",
        model_name="gpt-4o-mini",
        max_loops=1,
    ),
    Agent(
        agent_name="UX-Designer",
        system_prompt="User experience and design specialist",
        model_name="gpt-4o-mini",
        max_loops=1,
    ),
]

# Initialize swarm
swarm = RoundRobinSwarm(
    agents=agents,
    max_loops=3,
    output_type="final",  # "final", "dict", "list"
    verbose=True
)

# Run task
result = swarm.run(
    "Design a mobile app feature for real-time team collaboration"
)

print("=" * 60)
print("RESULT:")
print("=" * 60)
print(result)

Configuration Options

Parameter Default Description
agents Required List of Agent instances
max_loops 1 Number of discussion rounds
output_type "final" Output format: "final", "dict", "list"
verbose False Enable detailed logging
callback None Function called after each loop
max_retries 3 Maximum retry attempts per agent

Batch Processing

from swarms import Agent, RoundRobinSwarm

swarm = RoundRobinSwarm(
    agents=[agent1, agent2, agent3],
    max_loops=2
)

tasks = [
    "Evaluate option A for our product strategy",
    "Evaluate option B for our product strategy",
    "Compare options A and B",
]

results = swarm.run_batch(tasks)

for task, result in zip(tasks, results):
    print(f"\nTask: {task}")
    print(f"Result: {result[:150]}...")

Use Cases

Domain Example
Research Multiple agents contribute different research perspectives
Strategy Team discusses and refines strategic decisions
Design Iterative design review and refinement
Analysis Multi-perspective analysis of complex topics

How It Works

  1. Initialization: Task added to conversation history
  2. Loop Execution:
  3. Agents shuffled randomly for this loop
  4. Each agent receives full conversation history
  5. Collaborative prompt encourages building on others' insights
  6. Agent contributes unique perspective
  7. Iteration: Process repeats for max_loops rounds
  8. Result: Full conversation history with all contributions

Best Practices

  • Diverse Agents: Create agents with different specializations
  • Appropriate Loops: Use 2-3 loops for most collaborative tasks
  • Clear Roles: Give each agent a distinct expertise area
  • Monitor Progress: Use verbose=True during development

Next Steps