RoundRobinSwarm

Overview

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 and builds upon others’ responses collaboratively.

Installation

pip install -U swarms

Attributes

str

default:"RoundRobinSwarm"

Name of the swarm

str

Description of the swarm’s purpose

List[Agent]

required

List of agents in the swarm

bool

default:"False"

Flag to enable verbose mode

int

default:"1"

Maximum number of loops to run

callable

default:"None"

Callback function to be called after each loop

int

default:"3"

Maximum number of retries for agent execution

OutputType

default:"final"

Type of output format (“final”, “list”, “dict”, etc.)

Methods

run()

Executes the given task on the agents in a randomized round-robin fashion.

def run(self, task: str, *args, **kwargs) -> Union[str, dict, list]

Parameters:

task (str): The task to be executed

Returns: The result of the task execution in the specified output format

run_batch()

Execute multiple tasks sequentially through the round-robin swarm.

def run_batch(self, tasks: List[str]) -> List[Union[str, dict, list]]

Parameters:

tasks (List[str]): A list of task strings to be executed

Returns: List of results, one for each task

Usage Examples

Basic Round-Robin Execution

from swarms import Agent, RoundRobinSwarm

# Create specialized agents
agents = [
    Agent(
        agent_name="Analyst",
        system_prompt="You are a data analyst. Analyze information critically.",
        model_name="openai/gpt-4o",
    ),
    Agent(
        agent_name="Strategist",
        system_prompt="You are a strategist. Think about long-term implications.",
        model_name="openai/gpt-4o",
    ),
    Agent(
        agent_name="Implementer",
        system_prompt="You are an implementer. Focus on practical execution.",
        model_name="openai/gpt-4o",
    ),
]

# Create swarm
swarm = RoundRobinSwarm(
    agents=agents,
    max_loops=1,
    verbose=True
)

# Run a task
result = swarm.run("How should we approach building a new AI product?")
print(result)

Multiple Loops with Callback

def loop_callback(loop_num, result):
    print(f"Completed loop {loop_num + 1}")
    print(f"Last result: {result[:100]}...")

swarm = RoundRobinSwarm(
    agents=agents,
    max_loops=3,  # Each agent participates 3 times
    callback=loop_callback,
    verbose=True
)

result = swarm.run("Develop a comprehensive marketing strategy")

Batch Processing

tasks = [
    "Analyze market trends in AI",
    "Develop a product roadmap",
    "Create a risk assessment"
]

results = swarm.run_batch(tasks)

for i, result in enumerate(results):
    print(f"\nTask {i+1} Results:")
    print(result)

Different Output Types

# Get final message only
swarm_final = RoundRobinSwarm(
    agents=agents,
    output_type="final"
)
final_result = swarm_final.run("Analyze this problem")

# Get full conversation as dict
swarm_dict = RoundRobinSwarm(
    agents=agents,
    output_type="dict"
)
dict_result = swarm_dict.run("Analyze this problem")

# Get conversation as list
swarm_list = RoundRobinSwarm(
    agents=agents,
    output_type="list"
)
list_result = swarm_list.run("Analyze this problem")

Custom Agent Configuration

# Agents with different loop counts (set automatically)
swarm = RoundRobinSwarm(
    agents=agents,
    max_loops=2
)

# Each agent's max_loops is randomly set between 1-5 internally
result = swarm.run("Complex collaborative task")

How It Works

Initial Task: User provides a task to the swarm
Random Shuffling: Agents are randomly shuffled each loop for varied interactions
Context Building: Each agent receives the full conversation history
Collaborative Prompting: Agents are prompted to acknowledge and build upon previous responses
Multiple Loops: Process repeats for max_loops iterations
Final Output: Returns formatted output based on output_type

Collaborative Prompt Format

Each agent receives a prompt like:

[Previous conversation history]

As Analyst, you are agent 1 of 3 in this collaborative session. 
The other agents participating are: Strategist, Implementer.

Please review the conversation history above carefully and build upon 
the insights shared by other agents. Acknowledge their contributions 
where relevant and provide your unique perspective and expertise.

Your response:

Features

Randomized Order: Agents are shuffled each loop for dynamic interactions
Full Context: Each agent sees the complete conversation history
Collaborative: Agents are prompted to acknowledge and build on others’ work
Retry Logic: Automatic retries with exponential backoff on failures
Flexible Output: Multiple output format options
Batch Processing: Process multiple tasks efficiently
Callback Support: Execute custom logic after each loop
Error Handling: Comprehensive error handling and logging

Best Practices

Agent Diversity: Use agents with complementary skills and perspectives
Clear Prompts: Give agents specific roles in their system prompts
Loop Count: Start with 1-2 loops; increase if deeper collaboration needed
Output Type: Use “final” for simple tasks, “dict” for analysis of full conversation
Verbose Mode: Enable for debugging and understanding agent interactions

Core Classes

Multi-Agent Harnesses

Tools & Utilities

Deployment

RoundRobinSwarm

Overview

Installation

Attributes

Methods

run()

run_batch()

Usage Examples

Basic Round-Robin Execution

Multiple Loops with Callback

Batch Processing

Different Output Types

Custom Agent Configuration

How It Works

Collaborative Prompt Format

Features

Best Practices

​Overview

​Installation

​Attributes

​Methods

​run()

​run_batch()

​Usage Examples

​Basic Round-Robin Execution

​Multiple Loops with Callback

​Batch Processing

​Different Output Types

​Custom Agent Configuration

​How It Works

​Collaborative Prompt Format

​Features

​Best Practices

Overview

Installation

Attributes

Methods

run()

run_batch()

Usage Examples

Basic Round-Robin Execution

Multiple Loops with Callback

Batch Processing

Different Output Types

Custom Agent Configuration

How It Works

Collaborative Prompt Format

Features

Best Practices