Social Swarm Patterns

Low-level social algorithms in swarms.structs.swarming_architectures and swarms.structs.ma_blocks compose custom multi-agent flows before you reach full workflows like SequentialWorkflow or GraphWorkflow.

pip install -U swarms

For higher-level orchestration, see Social Algorithms and Agent Rearrange.

One sender broadcasts; all receivers process the shared context (broadcast is async):

import asyncio
from swarms import Agent
from swarms.structs.swarming_architectures import broadcast

sender = Agent(agent_name="Announcer", model_name="claude-sonnet-4-6", max_loops=1)
receivers = [
    Agent(agent_name="Analyst-A", model_name="claude-sonnet-4-6", max_loops=1),
    Agent(agent_name="Analyst-B", model_name="claude-sonnet-4-6", max_loops=1),
]

async def main():
    results = await broadcast(
        sender=sender,
        agents=receivers,
        task="Summarize Q4 priorities for your domain.",
    )
    print(results)

asyncio.run(main())

Agents pass work in a ring with shared conversation history:

from swarms import Agent
from swarms.structs.swarming_architectures import circular_swarm

agents = [
    Agent(agent_name="Researcher", model_name="claude-sonnet-4-6", max_loops=1),
    Agent(agent_name="Analyst", model_name="claude-sonnet-4-6", max_loops=1),
    Agent(agent_name="Writer", model_name="claude-sonnet-4-6", max_loops=1),
]

result = circular_swarm(agents=agents, tasks=["Draft a one-page market brief."])
print(result)

Shared task queue; workers drain tasks in round-robin order:

from swarms import Agent
from swarms.structs.swarming_architectures import mesh_swarm

agents = [
    Agent(agent_name=f"Worker-{i}", model_name="claude-sonnet-4-6", max_loops=1)
    for i in range(3)
]
tasks = ["Task A", "Task B", "Task C", "Task D"]

results = mesh_swarm(agents=agents, tasks=tasks)
print(results)

Agents in a square grid (list length should be a perfect square, e.g. 4 or 9):

from swarms import Agent
from swarms.structs.swarming_architectures import grid_swarm

agents = [
    Agent(agent_name=f"A-{i}", model_name="claude-sonnet-4-6", max_loops=1)
    for i in range(4)
]
result = grid_swarm(agents=agents, tasks=["Grid task 1", "Grid task 2"])
print(result)

First agent is the hub; it processes each task before the others:

from swarms import Agent
from swarms.structs.swarming_architectures import star_swarm

agents = [
    Agent(agent_name="Hub", model_name="claude-sonnet-4-6", max_loops=1),
    Agent(agent_name="Spoke-1", model_name="claude-sonnet-4-6", max_loops=1),
    Agent(agent_name="Spoke-2", model_name="claude-sonnet-4-6", max_loops=1),
]

result = star_swarm(agents=agents, tasks=["Coordinate subtasks and merge."])
print(result)

Agents arranged in a pyramid; tasks flow level by level:

from swarms import Agent
from swarms.structs.swarming_architectures import pyramid_swarm

agents = [
    Agent(agent_name="Director", model_name="claude-sonnet-4-6", max_loops=1),
    Agent(agent_name="Worker-1", model_name="claude-sonnet-4-6", max_loops=1),
    Agent(agent_name="Worker-2", model_name="claude-sonnet-4-6", max_loops=1),
]
result = pyramid_swarm(agents=agents, tasks=["Planning task"])
print(result)

Sender and receiver alternate on a single task:

from swarms import Agent
from swarms.structs.swarming_architectures import one_to_one

sender = Agent(agent_name="Sender", model_name="claude-sonnet-4-6", max_loops=1)
receiver = Agent(agent_name="Receiver", model_name="claude-sonnet-4-6", max_loops=1)

result = one_to_one(sender=sender, receiver=receiver, task="Review this draft section.")
print(result)

Run workers concurrently, then synthesize with an aggregator agent:

from swarms import Agent
from swarms.structs.ma_blocks import aggregate

workers = [
    Agent(agent_name=f"Expert-{i}", model_name="claude-sonnet-4-6", max_loops=1)
    for i in range(3)
]

merged = aggregate(workers=workers, task="Give one bullet each on risk, then merge.")
print(merged)

Choosing a pattern

Pattern	Best for
Broadcast	One announcement, many parallel responses
Circular	Sequential refinement with shared history
Mesh	Many independent tasks, worker pool
Grid	Local neighbor collaboration
Star	Central coordinator + specialists
Pyramid	Layered command structure
One-to-one	Single handoff between two agents
Aggregate	Fan-in synthesis of parallel outputs

Debate Swarm Quickstart Applications Overview

​Choosing a pattern

​Related

Choosing a pattern

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