This content originally appeared on DEV Community and was authored by Laura Salinas
In my last post I started introducing the concept of multi agent systems and gave a brief overview of the 5 patterns you can explore with Strands Agents 👇🏼
In this post we'll dive deeper into one of those patterns, Swarm. I'll share coding snippets and a the repo with examples that you can follow along.
What Is Swarm Intelligence?
Think about an ant colony: thousands of individual ants, each following simple rules, yet together they build complex structures, find optimal food sources, and adapt to challenges. No single ant directs the colony, yet collective intelligence emerges.
Agent swarms apply this same principle found in nature to artificial intelligence. Instead of one powerful agent trying to solve everything, we create multiple specialized agents that work together as a team. Each agent has its own expertise, but they share knowledge and can coordinate their efforts.
Key characteristics of agent swarms:
- Self-organizing teams with shared working memory
- Tool-based coordination between agents
- Autonomous collaboration without central control
- Dynamic task distribution based on agent capabilities
- Collective intelligence through shared context
This pattern often leads to emergent intelligence, or the idea that a group of specialized agents working together can solve problems more effectively than even the most sophisticated single agent. Why you may ask? Well the theory lies in the understanding that as agents work together, they're approaching unexpected behaviors, creativity, and problem-solving capabilities that were not explicitly coded by their developers.
How Swarms Work
In the Strands Agents framework, each agent in a swarm has access to four critical components:
- Full Task Context - Every agent sees the complete problem
- Agent History - They know which agents have worked on the task
- Shared Knowledge - They access insights from other agents
- Handoff Capability - They can transfer control to specialist agents
Coordination Mechanisms
Effective coordination is what separates a swarm from just multiple agents working separately. The Strands framework provides built-in coordination tools to make this easy to accomplish.
The Handoff Tool allows agents to transfer control precisely. For example, a handoff tool can give an agent named coder the context and let it implement the necessary Python code.
# Handoff Tool Description: Transfer control to another agent in the swarm for specialized help.
handoff_to_agent(
agent_name="coder",
message="I need help implementing this algorithm in Python",
context={"algorithm_details": "..."}
)
Shared context ensures every agent has the full picture. When an agent receives a handoff, they see:
- The handoff message explaining why they were called
- The original user request
- Which agents worked on this previously
- Shared knowledge from all previous agents
- List of other available specialists
Swarms in Strands also support multi-modal inputs—not just text, but images as well. This means your swarm can analyze diagrams, process reports, and generate a comprehensive analysis all in one workflow.
Here's an example of a multi-modal agent swarm:
from strands import Agent
from strands.multiagent import Swarm
from strands.types.content import ContentBlock
# Create agents for image processing workflow
image_analyzer = Agent(name="image_analyzer", system_prompt="You are an image analysis expert...")
report_writer = Agent(name="report_writer", system_prompt="You are a report writing expert...")
# Create the swarm
swarm = Swarm([image_analyzer, report_writer])
# Create content blocks with text and image
content_blocks = [
ContentBlock(text="Analyze this image and create a report about what you see:"),
ContentBlock(image={"format": "png", "source": {"bytes": image_bytes}}),
]
# Execute the swarm with multi-modal input
result = swarm(content_blocks)
Practical Implementation
Creating a swarm involves three key steps:
Step 1: Define Your Agents
# Create specialized agents
researcher = Agent(name="researcher", system_prompt="You are a research specialist...")
coder = Agent(name="coder", system_prompt="You are a coding specialist...")
reviewer = Agent(name="reviewer", system_prompt="You are a code review specialist...")
architect = Agent(name="architect", system_prompt="You are a system architecture specialist...")
Step 2: Create a swarm and configure behavior and safety parameters
# Create a swarm with these agents, starting with the researcher
swarm = Swarm(
[coder, researcher, reviewer, architect],
entry_point=researcher, # Start with the researcher
max_handoffs=20,
max_iterations=20,
execution_timeout=900.0, # 15 minutes
node_timeout=300.0, # 5 minutes per agent
repetitive_handoff_detection_window=8, # There must be >= 3 unique agents in the last 8 handoffs
repetitive_handoff_min_unique_agents=3
)
Step 3: Execute and Analyze Results
# Execute the swarm on a task
result = swarm("Design and implement a simple REST API for a todo app")
# Access node history
print(f"Node history: {[node.node_id for node in result.node_history]}")
The swarm constructor can return detailed results including execution status, which agents participated, individual agent outputs, performance metrics, and token usage statistics.
When to Use Swarm
A Swarm is ideal when your problem can be broken down into sub-tasks that benefit from different specialized perspectives. It's best for exploration, brainstorming, or synthesizing information from multiple sources through collaborative handoffs.
Context Management at Scale 📊
One challenge worth mentioning with swarms is managing shared context at scale. When every agent has access to the full context, things can get bloated quickly.
Strands has an interesting solution to this with the use_llm tool. This allows you to have dynamic LLMs that perform specific tasks and return just the results, keeping the main agent's context lean. You can check out more details in the Strands community built tools.
Best Practices 💡
From my exploration of swarm patterns, here are some best practices to keep in mind:
- Create specialized agents - Define clear roles for each agent in your swarm
- Set appropriate timeouts - Base these on task complexity and expected runtime
- Enable repetitive handoff detection - Prevent ping-pong behavior between agents
In the coming weeks we'll dive deeper into Graph and Agents as Tools patterns, so stay tuned for that!
Don't forget to give me a 🦄 if you got this far and let me know what pattern you're most excited to try in the comments!
Additional Resources 📚
This content originally appeared on DEV Community and was authored by Laura Salinas
Laura Salinas | Sciencx (2025-10-24T14:00:00+00:00) Strands Multi-Agent Systems: Swarm. Retrieved from https://www.scien.cx/2025/10/24/strands-multi-agent-systems-swarm/
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