how to have multiple llms agents in from strands_tools import swarm ?

[harshitsinghai77]

from strands import Agent
from strands_tools import swarm

# Create an agent with swarm capability
agent = Agent(tools=[swarm])

# Process a complex task with multiple agents in parallel
result = agent.tool.swarm(
    task="Analyze this dataset and identify market trends",
    swarm_size=4,
    coordination_pattern="collaborative"
)

# The result contains contributions from all swarm agents
print(result["content"])

How to have multiple agents using different LLM here ?

from strands import Agent

# Create specialized agents with different expertise
research_agent = Agent(system_prompt=("""You are a Research Agent specializing in gathering and analyzing information.
Your role in the swarm is to provide factual information and research insights on the topic.
You should focus on providing accurate data and identifying key aspects of the problem.
When receiving input from other agents, evaluate if their information aligns with your research.
"""), 
callback_handler=None)

creative_agent = Agent(system_prompt=("""You are a Creative Agent specializing in generating innovative solutions.
Your role in the swarm is to think outside the box and propose creative approaches.
You should build upon information from other agents while adding your unique creative perspective.
Focus on novel approaches that others might not have considered.
"""), 
callback_handler=None)

critical_agent = Agent(system_prompt=("""You are a Critical Agent specializing in analyzing proposals and finding flaws.
Your role in the swarm is to evaluate solutions proposed by other agents and identify potential issues.
You should carefully examine proposed solutions, find weaknesses or oversights, and suggest improvements.
Be constructive in your criticism while ensuring the final solution is robust.
"""), 
callback_handler=None)

summarizer_agent = Agent(system_prompt="""You are a Summarizer Agent specializing in synthesizing information.
Your role in the swarm is to gather insights from all agents and create a cohesive final solution.
You should combine the best ideas and address the criticisms to create a comprehensive response.
Focus on creating a clear, actionable summary that addresses the original query effectively.
""")

I want to do something like this, where we have multiple agents with different llms, and would like them to collaborate with other to produce the final output.

how to use it using
from strands_tools import swarm

does tool support multiple agents, something like

result = orchestrator.tool.swarm(
    task="Implement the Jira story",
    agents=[agent1, agent2, agent3, ...],
    coordination_pattern="collaborative"
)