swarms/docs/swarms/structs/various_execution_methods.md

Multi-Agent Execution API Reference

This comprehensive documentation covers all functions in the multi_agent_exec.py module for running multiple agents using various execution strategies. The module provides synchronous and asynchronous execution methods, optimized performance with uvloop, and utility functions for information retrieval.

Function Overview

Function	Signature	Category	Description
run_single_agent	run_single_agent(agent, task, *args, **kwargs) -> Any	Single Agent	Runs a single agent synchronously
run_agent_async	run_agent_async(agent, task) -> Any	Single Agent	Runs a single agent asynchronously using asyncio
run_agents_concurrently_async	run_agents_concurrently_async(agents, task) -> List[Any]	Concurrent Execution	Runs multiple agents concurrently using asyncio
run_agents_concurrently	run_agents_concurrently(agents, task, max_workers=None) -> List[Any]	Concurrent Execution	Optimized concurrent agent runner using ThreadPoolExecutor
run_agents_concurrently_multiprocess	run_agents_concurrently_multiprocess(agents, task, batch_size=None) -> List[Any]	Concurrent Execution	Manages agents concurrently in batches with optimized performance
batched_grid_agent_execution	batched_grid_agent_execution(agents, tasks, max_workers=None) -> List[Any]	Batched & Grid	Runs multiple agents with different tasks concurrently
run_agents_with_different_tasks	run_agents_with_different_tasks(agent_task_pairs, batch_size=10, max_workers=None) -> List[Any]	Batched & Grid	Runs agents with different tasks concurrently in batches
run_agents_concurrently_uvloop	run_agents_concurrently_uvloop(agents, task, max_workers=None) -> List[Any]	UVLoop Optimized	Runs agents concurrently using uvloop for optimized performance
run_agents_with_tasks_uvloop	run_agents_with_tasks_uvloop(agents, tasks, max_workers=None) -> List[Any]	UVLoop Optimized	Runs agents with different tasks using uvloop optimization
get_swarms_info	get_swarms_info(swarms) -> str	Utility	Fetches and formats information about available swarms
get_agents_info	get_agents_info(agents, team_name=None) -> str	Utility	Fetches and formats information about available agents

Single Agent Functions

run_single_agent(agent, task, *args, **kwargs)

Runs a single agent synchronously.

Signature

def run_single_agent(
    agent: AgentType,
    task: str,
    *args,
    **kwargs
) -> Any

Parameters

Parameter	Type	Required	Description
agent	AgentType	Yes	Agent instance to run
task	str	Yes	Task string to execute
*args	Any	No	Additional positional arguments
**kwargs	Any	No	Additional keyword arguments

Returns

• Any: Agent execution result

Example

from swarms.structs.agent import Agent
from swarms.structs.multi_agent_exec import run_single_agent

agent = Agent(
    agent_name="Financial-Analyst",
    system_prompt="You are a financial analysis expert",
    model_name="gpt-4o-mini",
    max_loops=1
)

result = run_single_agent(agent, "Analyze the current stock market trends")
print(result)

run_agent_async(agent, task)

Runs a single agent asynchronously using asyncio.

Signature

async def run_agent_async(agent: AgentType, task: str) -> Any

Parameters

Parameter	Type	Required	Description
agent	AgentType	Yes	Agent instance to run
task	str	Yes	Task string to execute

Returns

• Any: Agent execution result

Example

import asyncio
from swarms.structs.agent import Agent
from swarms.structs.multi_agent_exec import run_agent_async

async def main():
    agent = Agent(
        agent_name="Researcher",
        system_prompt="You are a research assistant",
        model_name="gpt-4o-mini",
        max_loops=1
    )

    result = await run_agent_async(agent, "Research AI advancements in 2024")
    print(result)

asyncio.run(main())

Concurrent Execution Functions

run_agents_concurrently_async(agents, task)

Runs multiple agents concurrently using asyncio.

Signature

async def run_agents_concurrently_async(
    agents: List[AgentType],
    task: str
) -> List[Any]

Parameters

Parameter	Type	Required	Description
agents	List[AgentType]	Yes	List of Agent instances to run concurrently
task	str	Yes	Task string to execute by all agents

Returns

• List[Any]: List of outputs from each agent

Example

import asyncio
from swarms.structs.agent import Agent
from swarms.structs.multi_agent_exec import run_agents_concurrently_async

async def main():
    agents = [
        Agent(
            agent_name=f"Analyst-{i}",
            system_prompt="You are a market analyst",
            model_name="gpt-4o-mini",
            max_loops=1
        )
        for i in range(3)
    ]

    task = "Analyze the impact of AI on job markets"
    results = await run_agents_concurrently_async(agents, task)

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

asyncio.run(main())

run_agents_concurrently(agents, task, max_workers=None)

Optimized concurrent agent runner using ThreadPoolExecutor.

Signature

def run_agents_concurrently(
    agents: List[AgentType],
    task: str,
    max_workers: Optional[int] = None,
) -> List[Any]

Parameters

Parameter	Type	Required	Default	Description
agents	List[AgentType]	Yes	-	List of Agent instances to run concurrently
task	str	Yes	-	Task string to execute
max_workers	Optional[int]	No	95% of CPU cores	Maximum number of threads in the executor

Returns

• List[Any]: List of outputs from each agent (exceptions included if agents fail)

Example

from swarms.structs.agent import Agent
from swarms.structs.multi_agent_exec import run_agents_concurrently

# Create multiple agents
agents = [
    Agent(
        agent_name="Tech-Analyst",
        system_prompt="You are a technology analyst",
        model_name="gpt-4o-mini",
        max_loops=1
    ),
    Agent(
        agent_name="Finance-Analyst",
        system_prompt="You are a financial analyst",
        model_name="gpt-4o-mini",
        max_loops=1
    ),
    Agent(
        agent_name="Market-Strategist",
        system_prompt="You are a market strategist",
        model_name="gpt-4o-mini",
        max_loops=1
    )
]

task = "Analyze the future of electric vehicles in 2025"
results = run_agents_concurrently(agents, task, max_workers=4)

for i, result in enumerate(results):
    print(f"Agent {i+1} ({agents[i].agent_name}): {result}")

run_agents_concurrently_multiprocess(agents, task, batch_size=None)

Manages and runs multiple agents concurrently in batches with optimized performance.

Signature

def run_agents_concurrently_multiprocess(
    agents: List[Agent],
    task: str,
    batch_size: int = os.cpu_count()
) -> List[Any]

Parameters

Parameter	Type	Required	Default	Description
agents	List[Agent]	Yes	-	List of Agent instances to run concurrently
task	str	Yes	-	Task string to execute by all agents
batch_size	int	No	CPU count	Number of agents to run in parallel in each batch

Returns

• List[Any]: List of outputs from each agent

Example

import os
from swarms.structs.agent import Agent
from swarms.structs.multi_agent_exec import run_agents_concurrently_multiprocess

agents = [
    Agent(
        agent_name=f"Research-Agent-{i}",
        system_prompt="You are a research specialist",
        model_name="gpt-4o-mini",
        max_loops=1
    )
    for i in range(5)
]

task = "Research the benefits of renewable energy"
batch_size = os.cpu_count()  # Use all CPU cores
results = run_agents_concurrently_multiprocess(agents, task, batch_size)

print(f"Completed {len(results)} agent executions")

Batched and Grid Execution

batched_grid_agent_execution(agents, tasks, max_workers=None)

Runs multiple agents with different tasks concurrently using batched grid execution.

Signature

def batched_grid_agent_execution(
    agents: List["AgentType"],
    tasks: List[str],
    max_workers: int = None,
) -> List[Any]

Parameters

Parameter	Type	Required	Default	Description
agents	List[AgentType]	Yes	-	List of agent instances
tasks	List[str]	Yes	-	List of tasks, one for each agent
max_workers	int	No	90% of CPU cores	Maximum number of threads to use

Returns

• List[Any]: List of results from each agent

Raises

• ValueError: If number of agents doesn't match number of tasks

Example

from swarms.structs.agent import Agent
from swarms.structs.multi_agent_exec import batched_grid_agent_execution

agents = [
    Agent(
        agent_name="Data-Scientist",
        system_prompt="You are a data science expert",
        model_name="gpt-4o-mini",
        max_loops=1
    ),
    Agent(
        agent_name="ML-Engineer",
        system_prompt="You are a machine learning engineer",
        model_name="gpt-4o-mini",
        max_loops=1
    ),
    Agent(
        agent_name="AI-Researcher",
        system_prompt="You are an AI researcher",
        model_name="gpt-4o-mini",
        max_loops=1
    )
]

tasks = [
    "Analyze machine learning algorithms performance",
    "Design a neural network architecture",
    "Research latest AI breakthroughs"
]

results = batched_grid_agent_execution(agents, tasks, max_workers=3)

for i, result in enumerate(results):
    print(f"Task {i+1}: {tasks[i]}")
    print(f"Result: {result}\n")

run_agents_with_different_tasks(agent_task_pairs, batch_size=10, max_workers=None)

Runs multiple agents with different tasks concurrently, processing them in batches.

Signature

def run_agents_with_different_tasks(
    agent_task_pairs: List[tuple["AgentType", str]],
    batch_size: int = 10,
    max_workers: int = None,
) -> List[Any]

Parameters

Parameter	Type	Required	Default	Description
agent_task_pairs	List[tuple[AgentType, str]]	Yes	-	List of (agent, task) tuples
batch_size	int	No	10	Number of agents to run in parallel in each batch
max_workers	int	No	None	Maximum number of threads

Returns

• List[Any]: List of outputs from each agent, in the same order as input pairs

Example

from swarms.structs.agent import Agent
from swarms.structs.multi_agent_exec import run_agents_with_different_tasks

# Create agents
agents = [
    Agent(
        agent_name="Content-Writer",
        system_prompt="You are a content writer",
        model_name="gpt-4o-mini",
        max_loops=1
    ),
    Agent(
        agent_name="Editor",
        system_prompt="You are an editor",
        model_name="gpt-4o-mini",
        max_loops=1
    ),
    Agent(
        agent_name="SEO-Specialist",
        system_prompt="You are an SEO specialist",
        model_name="gpt-4o-mini",
        max_loops=1
    )
]

# Create agent-task pairs
agent_task_pairs = [
    (agents[0], "Write a blog post about sustainable living"),
    (agents[1], "Edit and improve this article draft"),
    (agents[2], "Optimize this content for SEO")
]

results = run_agents_with_different_tasks(agent_task_pairs, batch_size=2)

for i, result in enumerate(results):
    agent, task = agent_task_pairs[i]
    print(f"{agent.agent_name} - {task}: {result}")

UVLoop Optimized Functions

run_agents_concurrently_uvloop(agents, task, max_workers=None)

Runs multiple agents concurrently using uvloop for optimized async performance.

Signature

def run_agents_concurrently_uvloop(
    agents: List[AgentType],
    task: str,
    max_workers: Optional[int] = None,
) -> List[Any]

Parameters

Parameter	Type	Required	Default	Description
agents	List[AgentType]	Yes	-	List of Agent instances to run concurrently
task	str	Yes	-	Task string to execute by all agents
max_workers	Optional[int]	No	95% of CPU cores	Maximum number of threads in the executor

Returns

• List[Any]: List of outputs from each agent

Raises

• ImportError: If uvloop is not installed
• RuntimeError: If uvloop cannot be set as the event loop policy

Example

from swarms.structs.agent import Agent
from swarms.structs.multi_agent_exec import run_agents_concurrently_uvloop

# Note: uvloop must be installed (pip install uvloop)
agents = [
    Agent(
        agent_name="Performance-Analyst",
        system_prompt="You are a performance analyst",
        model_name="gpt-4o-mini",
        max_loops=1
    )
    for _ in range(5)
]

task = "Analyze system performance metrics"
results = run_agents_concurrently_uvloop(agents, task)

print(f"Processed {len(results)} agents with uvloop optimization")

run_agents_with_tasks_uvloop(agents, tasks, max_workers=None)

Runs multiple agents with different tasks concurrently using uvloop.

Signature

def run_agents_with_tasks_uvloop(
    agents: List[AgentType],
    tasks: List[str],
    max_workers: Optional[int] = None,
) -> List[Any]

Parameters

Parameter	Type	Required	Default	Description
agents	List[AgentType]	Yes	-	List of Agent instances to run
tasks	List[str]	Yes	-	List of task strings (must match number of agents)
max_workers	Optional[int]	No	95% of CPU cores	Maximum number of threads

Returns

• List[Any]: List of outputs from each agent

Raises

• ValueError: If number of agents doesn't match number of tasks

• ImportError: If uvloop is not installed

• RuntimeError: If uvloop cannot be set as the event loop policy

Example

from swarms.structs.agent import Agent
from swarms.structs.multi_agent_exec import run_agents_with_tasks_uvloop

agents = [
    Agent(
        agent_name="Data-Analyst-1",
        system_prompt="You are a data analyst",
        model_name="gpt-4o-mini",
        max_loops=1
    ),
    Agent(
        agent_name="Data-Analyst-2",
        system_prompt="You are a data analyst",
        model_name="gpt-4o-mini",
        max_loops=1
    )
]

tasks = [
    "Analyze sales data from Q1 2024",
    "Analyze customer satisfaction metrics"
]

results = run_agents_with_tasks_uvloop(agents, tasks)

for i, result in enumerate(results):
    print(f"Task: {tasks[i]}")
    print(f"Result: {result}\n")

Utility Functions

get_swarms_info(swarms)

Fetches and formats information about all available swarms in the system.

Signature

def get_swarms_info(swarms: List[Callable]) -> str

Parameters

Parameter	Type	Required	Description
swarms	List[Callable]	Yes	List of swarm objects to get information about

Returns

• str: Formatted string containing names and descriptions of all swarms

Example

from swarms.structs.multi_agent_exec import get_swarms_info

# Assuming you have swarm objects
swarms = [
    # Your swarm objects here
]

info = get_swarms_info(swarms)
print(info)
# Output:
# Available Swarms:
#
# [Swarm 1]
# Name: ResearchSwarm
# Description: A swarm for research tasks
# Length of Agents: 3
# Swarm Type: hierarchical

get_agents_info(agents, team_name=None)

Fetches and formats information about all available agents in the system.

Signature

def get_agents_info(
    agents: List[Union[Agent, Callable]],
    team_name: str = None
) -> str

Parameters

Parameter	Type	Required	Default	Description
agents	List[Union[Agent, Callable]]	Yes	-	List of agent objects to get information about
team_name	str	No	None	Optional team name to display

Returns

• str: Formatted string containing names and descriptions of all agents

Example

from swarms.structs.agent import Agent
from swarms.structs.multi_agent_exec import get_agents_info

agents = [
    Agent(
        agent_name="Research-Agent",
        system_prompt="You are a research assistant",
        model_name="gpt-4o-mini",
        max_loops=2,
        role="Researcher"
    ),
    Agent(
        agent_name="Analysis-Agent",
        system_prompt="You are a data analyst",
        model_name="gpt-4o-mini",
        max_loops=1,
        role="Analyst"
    )
]

info = get_agents_info(agents, team_name="Data Team")
print(info)
# Output:
# Available Agents for Team: Data Team
#
# [Agent 1]
# Name: Research-Agent
# Description: You are a research assistant
# Role: Researcher
# Model: gpt-4o-mini
# Max Loops: 2
#
# [Agent 2]
# Name: Analysis-Agent
# Description: You are a data analyst
# Role: Analyst
# Model: gpt-4o-mini
# Max Loops: 1

Complete Usage Examples

Advanced Multi-Agent Workflow Example

import asyncio
from swarms.structs.agent import Agent
from swarms.structs.multi_agent_exec import (
    run_agents_concurrently,
    run_agents_with_different_tasks,
    batched_grid_agent_execution,
    get_agents_info
)

# Create specialized agents
agents = [
    Agent(
        agent_name="Market-Researcher",
        system_prompt="You are a market research expert specializing in consumer behavior",
        model_name="gpt-4o-mini",
        max_loops=1,
        role="Researcher"
    ),
    Agent(
        agent_name="Data-Analyst",
        system_prompt="You are a data analyst expert in statistical analysis",
        model_name="gpt-4o-mini",
        max_loops=1,
        role="Analyst"
    ),
    Agent(
        agent_name="Strategy-Consultant",
        system_prompt="You are a strategy consultant specializing in business development",
        model_name="gpt-4o-mini",
        max_loops=1,
        role="Consultant"
    ),
    Agent(
        agent_name="Financial-Advisor",
        system_prompt="You are a financial advisor specializing in investment strategies",
        model_name="gpt-4o-mini",
        max_loops=1,
        role="Advisor"
    )
]

# Display agent information
print("=== Agent Information ===")
print(get_agents_info(agents, "Business Intelligence Team"))
print("\n" + "="*50 + "\n")

# Example 1: Same task for all agents
print("=== Example 1: Concurrent Execution with Same Task ===")
task = "Analyze the impact of remote work trends on commercial real estate market in 2024"
results = run_agents_concurrently(agents, task, max_workers=4)

for i, result in enumerate(results):
    print(f"\n{agents[i].agent_name} Analysis:")
    print(f"Result: {result}")

print("\n" + "="*50 + "\n")

# Example 2: Different tasks for different agents
print("=== Example 2: Different Tasks for Different Agents ===")
agent_task_pairs = [
    (agents[0], "Research consumer preferences for electric vehicles"),
    (agents[1], "Analyze sales data for EV market penetration"),
    (agents[2], "Develop marketing strategy for EV adoption"),
    (agents[3], "Assess financial viability of EV charging infrastructure")
]

results = run_agents_with_different_tasks(agent_task_pairs, batch_size=2)

for i, result in enumerate(results):
    agent, task = agent_task_pairs[i]
    print(f"\n{agent.agent_name} - Task: {task}")
    print(f"Result: {result}")

print("\n" + "="*50 + "\n")

# Example 3: Grid execution with matched agents and tasks
print("=== Example 3: Batched Grid Execution ===")
grid_agents = agents[:3]  # Use first 3 agents
grid_tasks = [
    "Forecast market trends for renewable energy",
    "Evaluate risk factors in green technology investments",
    "Compare traditional vs sustainable investment portfolios"
]

grid_results = batched_grid_agent_execution(grid_agents, grid_tasks, max_workers=3)

for i, result in enumerate(grid_results):
    print(f"\nTask {i+1}: {grid_tasks[i]}")
    print(f"Agent: {grid_agents[i].agent_name}")
    print(f"Result: {result}")

print("\n=== Workflow Complete ===")

Error Handling and Best Practices

from swarms.structs.agent import Agent
from swarms.structs.multi_agent_exec import run_agents_concurrently
import logging

# Set up logging
logging.basicConfig(level=logging.INFO)

# Create agents with error handling
agents = [
    Agent(
        agent_name=f"Agent-{i}",
        system_prompt="You are a helpful assistant",
        model_name="gpt-4o-mini",
        max_loops=1
    )
    for i in range(5)
]

task = "Perform a complex analysis task"

try:
    results = run_agents_concurrently(agents, task, max_workers=4)

    # Handle results (some may be exceptions)
    for i, result in enumerate(results):
        if isinstance(result, Exception):
            print(f"Agent {i+1} failed with error: {result}")
        else:
            print(f"Agent {i+1} succeeded: {result}")

except Exception as e:
    print(f"Execution failed: {e}")

# Best practices:
# 1. Always handle exceptions in results
# 2. Use appropriate max_workers based on system resources
# 3. Monitor memory usage for large agent counts
# 4. Consider batch processing for very large numbers of agents
# 5. Use uvloop functions for I/O intensive tasks

Performance Considerations

Technique	Best Use Case / Description
ThreadPoolExecutor	Best for CPU-bound tasks with moderate I/O
uvloop	Optimized for I/O-bound tasks, significantly faster than standard asyncio
Batch Processing	Prevents system overload with large numbers of agents
Resource Monitoring	Adjust worker counts based on system capabilities
Async/Await	Use async functions for better concurrency control