swarms
The Enterprise-Grade Multi-Agent Orchestration Framework. Website: https://swarms.ai
Install / Use
npx skills add kyegomez/swarmsInstalls into whichever agent you are using.
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View source on GitHubOverview
Swarms, The Enterprise-Grade Production-Ready Multi-Agent Orchestration Framework
Swarms is the most reliable, scalable, and adaptive multi-agent orchestration framework available today. We provide a comprehensive suite of production-ready, prebuilt multi-agent architectures, including sequential, concurrent, and hierarchical systems. Additionally, Swarms offers backward compatibility with leading agent frameworks and interoperability with protocols such as MCP, x402, skills, and much more.
Install
Using pip
$ pip3 install -U swarms
Using uv (Recommended)
uv is a fast Python package installer and resolver, written in Rust.
$ uv pip install swarms
Using poetry
$ poetry add swarms
From source
# Clone the repository
$ git clone https://github.com/kyegomez/swarms.git
$ cd swarms
$ pip install -r requirements.txt
Environment Configuration
Learn more about the environment configuration here
OPENAI_API_KEY=""
WORKSPACE_DIR="agent_workspace"
ANTHROPIC_API_KEY=""
GROQ_API_KEY=""
Your First Agent
An Agent is the fundamental building block of a swarm—an autonomous entity powered by an LLM + Tools + Memory. Learn more Here
from swarms import Agent
# Initialize a new agent
agent = Agent(
model_name="gpt-5.4", # Specify the LLM
max_loops="auto", # Set the number of interactions
interactive=True, # Enable interactive mode for real-time feedback
temperature=None,
)
# Run the agent with a task
agent.run("What are the key benefits of using a multi-agent system?")
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
MCP Integration
The Model Context Protocol (MCP) lets agents easily access external tools and data by pointing to an MCP server URL, which automatically provides tools to the agent as needed. Agents become MCP-enabled by setting mcp_url or mcp_urls, and can use tools from one or many servers with no manual configuration. Free and public MCP servers like DeepWiki work out of the box, offering immediate access to useful agent tools.
from swarms import Agent
agent = Agent(
agent_name="MCP-Agent",
model_name="claude-sonnet-5",
mcp_url="https://mcp.deepwiki.com/mcp",
max_loops=1,
temperature=None,
max_tokens=16_000,
reasoning_effort=None,
)
print(
agent.run(
"Use your tools to explain what the kyegomez/swarms repository does."
)
)
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. |
Learn more about all of the 60+ Multi-Agent Structures we have available here
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 tasks with dependencies are handled correctly.
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)
ConcurrentWorkflow
A ConcurrentWorkflow runs multiple agents simultaneously, allowing for parallel execution of tasks. This architecture drastically reduces execution time for tasks t
Truncated for display — read the full file on GitHub.
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