swarms

Autonomous Agent with max_loops="auto"

Setting max_loops="auto" lets the agent decide for itself when the task is complete — it keeps reasoning and acting until it reaches a stopping condition, rather than halting after a fixed number of iterations. This is the recommended mode for open-ended, multi-step tasks where the number of steps isn't known in advance.

from swarms import Agent

agent = Agent(
    agent_name="Autonomous-Research-Agent",
    agent_description="An autonomous agent that conducts multi-step research independently.",
    system_prompt=(
        "You are an autonomous research agent. Break down complex tasks into steps, "
        "execute each step thoroughly, and signal completion only when the full task is done."
    ),
    model_name="gpt-5.4",
    max_loops="auto",       # Agent decides when it's done — no fixed iteration cap
    autosave=True,
    verbose=True,
)

# The agent will keep looping — planning, executing, and reflecting — until it
# determines the task is fully complete.
result = agent.run(
    "Research the current state of quantum computing, identify the top three "
    "hardware approaches, and summarize the key challenges each faces."
)
print(result)

When to use max_loops="auto":

• Open-ended research or analysis tasks
• Tasks that require iterative refinement (e.g., write → review → revise)
• Any workflow where the number of steps depends on intermediate results

When to use a fixed max_loops value:

• Latency-sensitive or cost-sensitive production pipelines
• Tasks with a well-defined, bounded number of steps

Your First Swarm: Multi-Agent Collaboration

A Swarm consists of multiple agents working together. This simple example creates a two-agent workflow for researching and writing a blog post. Learn More About SequentialWorkflow

from swarms import Agent, SequentialWorkflow

# Agent 1: The Researcher
researcher = Agent(
    agent_name="Researcher",
    system_prompt="Your job is to research the provided topic and provide a detailed summary.",
    model_name="gpt-5.4",
)

# Agent 2: The Writer
writer = Agent(
    agent_name="Writer",
    system_prompt="Your job is to take the research summary and write a beautiful, engaging blog post about it.",
    model_name="gpt-5.4",
)

# Create a sequential workflow where the researcher's output feeds into the writer's input
workflow = SequentialWorkflow(agents=[researcher, writer])

# Run the workflow on a task
final_post = workflow.run("The history and future of artificial intelligence")
print(final_post)

Available Multi-Agent Architectures

swarms provides a variety of powerful, pre-built multi-agent architectures enabling you to orchestrate agents in various ways. Choose the right structure for your specific problem to build efficient and reliable production systems.

| Architecture | Description | Best For | |---|---|---| | SequentialWorkflow | Agents execute tasks in a linear chain; the output of one agent becomes the input for the next. | Step-by-step processes such as data transformation pipelines and report generation. | | ConcurrentWorkflow | Agents run tasks simultaneously for maximum efficiency. | High-throughput tasks such as batch processing and parallel data analysis. | | AgentRearrange | Dynamically maps complex relationships (e.g., a -> b, c) between agents. | Flexible and adaptive workflows, task distribution, and dynamic routing. | | GraphWorkflow | Orchestrates agents as nodes in a Directed Acyclic Graph (DAG). | Complex projects with intricate dependencies, such as software builds. | | MixtureOfAgents (MoA) | Utilizes multiple expert agents in parallel and synthesizes their outputs. | Complex problem-solving and achieving state-of-the-art performance through collaboration. | | GroupChat | Agents collaborate and make decisions through a conversational interface. | Real-time collaborative decision-making, negotiations, and brainstorming. | | ForestSwarm | Dynamically selects the most suitable agent or tree of agents for a given task. | Task routing, optimizing for expertise, and complex decision-making trees. | | HierarchicalSwarm | Orchestrates agents with a director who creates plans and distributes tasks to specialized worker agents. | Complex project management, team coordination, and hierarchical decision-making with feedback loops. | | HeavySwarm | Implements a five-phase workflow with specialized agents (Research, Analysis, Alternatives, Verification) for comprehensive task analysis. | Complex research and analysis tasks, financial analysis, strategic planning, and comprehensive reporting. | | SwarmRouter | A universal orchestrator that provides a single interface to run any type of swarm with dynamic selection. | Simplifying complex workflows, switching between swarm strategies, and unified multi-agent management. |

SequentialWorkflow

A SequentialWorkflow executes tasks in a strict order, forming a pipeline where each agent builds upon the work of the previous one. SequentialWorkflow is Ideal for processes that have clear, ordered steps. This ensures that