The Enterprise-Grade Production-Ready Multi-Agent Orchestration Framework

Swarms Website • Documentation • Swarms Marketplace

Features

Swarms delivers a comprehensive, enterprise-grade multi-agent infrastructure platform designed for production-scale deployments and seamless integration with existing systems. Learn more about the swarms feature set here

Category	Features	Benefits
Enterprise Architecture	• Production-Ready Infrastructure • High Availability Systems • Modular Microservices Design • Comprehensive Observability • Backwards Compatibility	• 99.9%+ Uptime Guarantee • Reduced Operational Overhead • Seamless Legacy Integration • Enhanced System Monitoring • Risk-Free Migration Path
Multi-Agent Orchestration	• Hierarchical Agent Swarms • Parallel Processing Pipelines • Sequential Workflow Orchestration • Graph-Based Agent Networks • Dynamic Agent Composition • Agent Registry Management	• Complex Business Process Automation • Scalable Task Distribution • Flexible Workflow Adaptation • Optimized Resource Utilization • Centralized Agent Governance • Enterprise-Grade Agent Lifecycle Management
Enterprise Integration	• Multi-Model Provider Support • Custom Agent Development Framework • Extensive Enterprise Tool Library • Multiple Memory Systems • Backwards Compatibility with LangChain, AutoGen, CrewAI • Standardized API Interfaces	• Vendor-Agnostic Architecture • Custom Solution Development • Extended Functionality Integration • Enhanced Knowledge Management • Seamless Framework Migration • Reduced Integration Complexity
Enterprise Scalability	• Concurrent Multi-Agent Processing • Intelligent Resource Management • Load Balancing & Auto-Scaling • Horizontal Scaling Capabilities • Performance Optimization • Capacity Planning Tools	• High-Throughput Processing • Cost-Effective Resource Utilization • Elastic Scaling Based On Demand • Linear Performance Scaling • Optimized Response Times • Predictable Growth Planning
Developer Experience	• Intuitive Enterprise API • Comprehensive Documentation • Active Enterprise Community • CLI & SDK Tools • IDE Integration Support • Code Generation Templates	• Accelerated Development Cycles • Reduced Learning Curve • Expert Community Support • Rapid Deployment Capabilities • Enhanced Developer Productivity • Standardized Development Patterns

Supported Protocols & Integrations

Swarms seamlessly integrates with industry-standard protocols and open specifications, unlocking powerful capabilities for tool integration, payment processing, distributed agent orchestration, and model interoperability.

Protocol	Description	Documentation
MCP (Model Context Protocol)	Standardized protocol for AI agents to interact with external tools and services through MCP servers. Enables dynamic tool discovery and execution.	MCP Integration Guide
X402	Cryptocurrency payment protocol for API endpoints. Enables monetization of agents with pay-per-use models.	X402 Quickstart
AOP (Agent Orchestration Protocol)	Framework for deploying and managing agents as distributed services. Enables agent discovery, management, and execution through standardized protocols.	AOP Reference
Swarms Marketplace	Platform for discovering and sharing production-ready prompts, agents, and tools. Enables automatic prompt loading from the marketplace and publishing your own prompts directly from code.	Marketplace Tutorial
Open Responses	Open-source specification and ecosystem for multi-provider, interoperable LLM interfaces based on the OpenAI Responses API. Provides a unified schema and tooling for calling language models, streaming results, and composing agentic workflows—independent of provider.	Open Responses Website
Agent Skills	Lightweight, markdown-based format for defining modular, reusable agent capabilities introduced by Anthropic. Enables specialization of agents without modifying code by loading skill definitions from simple SKILL.md files.	Agent Skills Documentation

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
)

# 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

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 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 that can be performed in parallel, making it ideal for high-throughput scenarios where agents work on similar tasks concurrently.

from swarms import Agent, ConcurrentWorkflow

# Create agents for different analysis tasks
market_analyst = Agent(
    agent_name="Market-Analyst",
    system_prompt="Analyze market trends and provide insights on the given topic.",
    model_name="gpt-5.4",
    max_loops=1,
)

financial_analyst = Agent(
    agent_name="Financial-Analyst", 
    system_prompt="Provide financial analysis and recommendations on the given topic.",
    model_name="gpt-5.4",
    max_loops=1,
)

risk_analyst = Agent(
    agent_name="Risk-Analyst",
    system_prompt="Assess risks and provide risk management strategies for the given topic.",
    model_name="gpt-5.4", 
    max_loops=1,
)

# Create concurrent workflow
concurrent_workflow = ConcurrentWorkflow(
    agents=[market_analyst, financial_analyst, risk_analyst],
    max_loops=1,
)

# Run all agents concurrently on the same task
results = concurrent_workflow.run(
    "Analyze the potential impact of AI technology on the healthcare industry"
)

print(results)

AgentRearrange

Inspired by einsum, AgentRearrange lets you define complex, non-linear relationships between agents using a simple string-based syntax. Learn more. This architecture is perfect for orchestrating dynamic workflows where agents might work in parallel, in sequence, or in any combination you choose.

from swarms import Agent, AgentRearrange

# Define agents
researcher = Agent(agent_name="researcher", model_name="gpt-5.4")
writer = Agent(agent_name="writer", model_name="gpt-5.4")
editor = Agent(agent_name="editor", model_name="gpt-5.4")

# Define a flow: researcher sends work to both writer and editor simultaneously
# This is a one-to-many relationship
flow = "researcher -> writer, editor"

# Create the rearrangement system
rearrange_system = AgentRearrange(
    agents=[researcher, writer, editor],
    flow=flow,
)

# Run the swarm
outputs = rearrange_system.run("Analyze the impact of AI on modern cinema.")
print(outputs)

SwarmRouter: The Universal Swarm Orchestrator

The SwarmRouter simplifies building complex workflows by providing a single interface to run any type of swarm. Instead of importing and managing different swarm classes, you can dynamically select the one you need just by changing the swarm_type parameter. Read the full documentation

This makes your code cleaner and more flexible, allowing you to switch between different multi-agent strategies with ease. Here's a complete example that shows how to define agents and then use SwarmRouter to execute the same task using different collaborative strategies.

from swarms import Agent
from swarms.structs.swarm_router import SwarmRouter, SwarmType

# Define a few generic agents
writer = Agent(agent_name="Writer", system_prompt="You are a creative writer.", model_name="gpt-5.4")
editor = Agent(agent_name="Editor", system_prompt="You are an expert editor for stories.", model_name="gpt-5.4")
reviewer = Agent(agent_name="Reviewer", system_prompt="You are a final reviewer who gives a score.", model_name="gpt-5.4")

# The agents and task will be the same for all examples
agents = [writer, editor, reviewer]
task = "Write a short story about a robot who discovers music."

# --- Example 1: SequentialWorkflow ---
# Agents run one after another in a chain: Writer -> Editor -> Reviewer.
print("Running a Sequential Workflow...")
sequential_router = SwarmRouter(swarm_type=SwarmType.SequentialWorkflow, agents=agents)
sequential_output = sequential_router.run(task)
print(f"Final Sequential Output:\n{sequential_output}\n")

# --- Example 2: ConcurrentWorkflow ---
# All agents receive the same initial task and run at the same time.
print("Running a Concurrent Workflow...")
concurrent_router = SwarmRouter(swarm_type=SwarmType.ConcurrentWorkflow, agents=agents)
concurrent_outputs = concurrent_router.run(task)
# This returns a dictionary of each agent's output
for agent_name, output in concurrent_outputs.items():
    print(f"Output from {agent_name}:\n{output}\n")

# --- Example 3: MixtureOfAgents ---
# All agents run in parallel, and a special 'aggregator' agent synthesizes their outputs.
print("Running a Mixture of Agents Workflow...")
aggregator = Agent(
    agent_name="Aggregator",
    system_prompt="Combine the story, edits, and review into a final document.",
    model_name="gpt-5.4"
)
moa_router = SwarmRouter(
    swarm_type=SwarmType.MixtureOfAgents,
    agents=agents,
    aggregator_agent=aggregator, # MoA requires an aggregator
)
aggregated_output = moa_router.run(task)
print(f"Final Aggregated Output:\n{aggregated_output}\n")

The SwarmRouter is a powerful tool for simplifying multi-agent orchestration. It provides a consistent and flexible way to deploy different collaborative strategies, allowing you to build more sophisticated applications with less code.

AutoSwarmBuilder: Autonomous Agent Generation

The AutoSwarmBuilder automatically generates specialized agents and their workflows based on your task description. Simply describe what you need, and it will create a complete multi-agent system with detailed prompts and optimal agent configurations. Learn more about AutoSwarmBuilder

from swarms.structs.auto_swarm_builder import AutoSwarmBuilder
import json

# Initialize the AutoSwarmBuilder
swarm = AutoSwarmBuilder(
    name="My Swarm",
    description="A swarm of agents",
    verbose=True,
    max_loops=1,
    return_agents=True,
    model_name="gpt-5.4",
)

# Let the builder automatically create agents and workflows
result = swarm.run(
    task="Create an accounting team to analyze crypto transactions, "
         "there must be 5 agents in the team with extremely extensive prompts. "
         "Make the prompts extremely detailed and specific and long and comprehensive. "
         "Make sure to include all the details of the task in the prompts."
)

# The result contains the generated agents and their configurations
print(json.dumps(result, indent=4))

The AutoSwarmBuilder provides:

Automatic Agent Generation: Creates specialized agents based on task requirements
Intelligent Prompt Engineering: Generates comprehensive, detailed prompts for each agent
Optimal Workflow Design: Determines the best agent interactions and workflow structure
Production-Ready Configurations: Returns fully configured agents ready for deployment
Flexible Architecture: Supports various swarm types and agent specializations

This feature is perfect for rapid prototyping, complex task decomposition, and creating specialized agent teams without manual configuration.

MixtureOfAgents (MoA)

The MixtureOfAgents architecture processes tasks by feeding them to multiple "expert" agents in parallel. Their diverse outputs are then synthesized by an aggregator agent to produce a final, high-quality result. Learn more here

from swarms import Agent, MixtureOfAgents

# Define expert agents
financial_analyst = Agent(agent_name="FinancialAnalyst", system_prompt="Analyze financial data.", model_name="gpt-5.4")
market_analyst = Agent(agent_name="MarketAnalyst", system_prompt="Analyze market trends.", model_name="gpt-5.4")
risk_analyst = Agent(agent_name="RiskAnalyst", system_prompt="Analyze investment risks.", model_name="gpt-5.4")

# Define the aggregator agent
aggregator = Agent(
    agent_name="InvestmentAdvisor",
    system_prompt="Synthesize the financial, market, and risk analyses to provide a final investment recommendation.",
    model_name="gpt-5.4"
)

# Create the MoA swarm
moa_swarm = MixtureOfAgents(
    agents=[financial_analyst, market_analyst, risk_analyst],
    aggregator_agent=aggregator,
)

# Run the swarm
recommendation = moa_swarm.run("Should we invest in NVIDIA stock right now?")
print(recommendation)

GroupChat

GroupChat creates a conversational environment where multiple agents can interact, discuss, and collaboratively solve a problem. You can define the speaking order or let it be determined dynamically. This architecture is ideal for tasks that benefit from debate and multi-perspective reasoning, such as contract negotiation, brainstorming, or complex decision-making.

from swarms import Agent, GroupChat

# Define agents for a debate
tech_optimist = Agent(agent_name="TechOptimist", system_prompt="Argue for the benefits of AI in society.", model_name="gpt-5.4")
tech_critic = Agent(agent_name="TechCritic", system_prompt="Argue against the unchecked advancement of AI.", model_name="gpt-5.4")

# Create the group chat
chat = GroupChat(
    agents=[tech_optimist, tech_critic],
    max_loops=4, # Limit the number of turns in the conversation
)

# Run the chat with an initial topic
conversation_history = chat.run(
    "Let's discuss the societal impact of artificial intelligence."
)

# Print the full conversation
for message in conversation_history:
    print(f"[{message['agent_name']}]: {message['content']}")

HierarchicalSwarm

HierarchicalSwarm implements a director-worker pattern where a central director agent creates comprehensive plans and distributes specific tasks to specialized worker agents. The director evaluates results and can issue new orders in feedback loops, making it ideal for complex project management and team coordination scenarios.

from swarms import Agent, HierarchicalSwarm

# Define specialized worker agents
content_strategist = Agent(
    agent_name="Content-Strategist",
    system_prompt="You are a senior content strategist. Develop comprehensive content strategies, editorial calendars, and content roadmaps.",
    model_name="gpt-5.4"
)

creative_director = Agent(
    agent_name="Creative-Director", 
    system_prompt="You are a creative director. Develop compelling advertising concepts, visual directions, and campaign creativity.",
    model_name="gpt-5.4"
)

seo_specialist = Agent(
    agent_name="SEO-Specialist",
    system_prompt="You are an SEO expert. Conduct keyword research, optimize content, and develop organic growth strategies.",
    model_name="gpt-5.4"
)

brand_strategist = Agent(
    agent_name="Brand-Strategist",
    system_prompt="You are a brand strategist. Develop brand positioning, identity systems, and market differentiation strategies.",
    model_name="gpt-5.4"
)

# Create the hierarchical swarm with a director
marketing_swarm = HierarchicalSwarm(
    name="Marketing-Team-Swarm",
    description="A comprehensive marketing team with specialized agents coordinated by a director",
    agents=[content_strategist, creative_director, seo_specialist, brand_strategist],
    max_loops=2,  # Allow for feedback and refinement
    verbose=True
)

# Run the swarm on a complex marketing challenge
result = marketing_swarm.run(
    "Develop a comprehensive marketing strategy for a new SaaS product launch. "
    "The product is a project management tool targeting small to medium businesses. "
    "Coordinate the team to create content strategy, creative campaigns, SEO optimization, "
    "and brand positioning that work together cohesively."
)

print(result)

The HierarchicalSwarm excels at:

Complex Project Management: Breaking down large tasks into specialized subtasks
Team Coordination: Ensuring all agents work toward unified goals
Quality Control: Director provides feedback and refinement loops
Scalable Workflows: Easy to add new specialized agents as needed

HeavySwarm

HeavySwarm implements a sophisticated 5-phase workflow inspired by X.AI's Grok heavy implementation. It uses specialized agents (Research, Analysis, Alternatives, Verification) to provide comprehensive task analysis through intelligent question generation, parallel execution, and synthesis. This architecture excels at complex research and analysis tasks requiring thorough investigation and multiple perspectives.

from swarms import HeavySwarm

# Pip install swarms-tools
from swarms_tools import exa_search

swarm = HeavySwarm(
    name="Gold ETF Research Team",
    description="A team of agents that research the best gold ETFs",
    worker_model_name="claude-sonnet-4-20250514",
    show_dashboard=True,
    question_agent_model_name="gpt-4.1",
    loops_per_agent=1,
    agent_prints_on=False,
    worker_tools=[exa_search],
    random_loops_per_agent=True,
)

prompt = (
    "Find the best 3 gold ETFs. For each ETF, provide the ticker symbol, "
    "full name, current price, expense ratio, assets under management, and "
    "a brief explanation of why it is considered among the best. Present the information "
    "in a clear, structured format suitable for investors. Scrape the data from the web. "
)

out = swarm.run(prompt)
print(out)

The HeavySwarm provides:

5-Phase Analysis: Question generation, research, analysis, alternatives, and verification
Specialized Agents: Each phase uses purpose-built agents for optimal results
Comprehensive Coverage: Multiple perspectives and thorough investigation
Real-time Dashboard: Optional visualization of the analysis process
Structured Output: Well-organized and actionable results

This architecture is perfect for financial analysis, strategic planning, research reports, and any task requiring deep, multi-faceted analysis. Learn more about HeavySwarm

Social Algorithms

Social Algorithms provide a flexible framework for defining custom communication patterns between agents. You can upload any arbitrary social algorithm as a callable that defines the sequence of communication, enabling agents to talk to each other in sophisticated ways. Learn more about Social Algorithms

from swarms import Agent, SocialAlgorithms

# Define a custom social algorithm
def research_analysis_synthesis_algorithm(agents, task, **kwargs):
    # Agent 1 researches the topic
    research_result = agents[0].run(f"Research: {task}")
    
    # Agent 2 analyzes the research
    analysis = agents[1].run(f"Analyze this research: {research_result}")
    
    # Agent 3 synthesizes the findings
    synthesis = agents[2].run(f"Synthesize: {research_result} + {analysis}")
    
    return {
        "research": research_result,
        "analysis": analysis,
        "synthesis": synthesis
    }

# Create agents
researcher = Agent(
  agent_name="Researcher",
  agent_description="Expert in comprehensive research and information gathering.",
  model_name="gpt-4.1"
)
analyst = Agent(
  agent_name="Analyst",
  agent_description="Specialist in analyzing and interpreting data.",
  model_name="gpt-4.1"
)
synthesizer = Agent(
  agent_name="Synthesizer",
  agent_description="Focused on synthesizing and integrating research insights.",
  model_name="gpt-4.1"
)

# Create social algorithm
social_alg = SocialAlgorithms(
    name="Research-Analysis-Synthesis",
    agents=[researcher, analyst, synthesizer],
    social_algorithm=research_analysis_synthesis_algorithm,
    verbose=True
)

# Run the algorithm
result = social_alg.run("The impact of AI on healthcare")
print(result.final_outputs)

Perfect for implementing complex multi-agent workflows, collaborative problem-solving, and custom communication protocols.

Agent Orchestration Protocol (AOP)

The Agent Orchestration Protocol (AOP) is a powerful framework for deploying and managing agents as distributed services. AOP enables agents to be discovered, managed, and executed through a standardized protocol, making it perfect for building scalable multi-agent systems. Learn more about AOP

from swarms import Agent
from swarms.structs.aop import AOP

# Create specialized agents
research_agent = Agent(
    agent_name="Research-Agent",
    agent_description="Expert in research and data collection",
    model_name="anthropic/claude-sonnet-4-5",
    max_loops=1,
    tags=["research", "data-collection", "analysis"],
    capabilities=["web-search", "data-gathering", "report-generation"],
    role="researcher"
)

analysis_agent = Agent(
    agent_name="Analysis-Agent", 
    agent_description="Expert in data analysis and insights",
    model_name="anthropic/claude-sonnet-4-5",
    max_loops=1,
    tags=["analysis", "data-processing", "insights"],
    capabilities=["statistical-analysis", "pattern-recognition", "visualization"],
    role="analyst"
)

# Create AOP server
deployer = AOP(
    server_name="ResearchCluster",
    port=8000,
    verbose=True
)

# Add agents to the server
deployer.add_agent(
    agent=research_agent,
    tool_name="research_tool",
    tool_description="Research and data collection tool",
    timeout=30,
    max_retries=3
)

deployer.add_agent(
    agent=analysis_agent,
    tool_name="analysis_tool", 
    tool_description="Data analysis and insights tool",
    timeout=30,
    max_retries=3
)

# List all registered agents
print("Registered agents:", deployer.list_agents())

# Start the AOP server
deployer.run()

Perfect for deploying large scale multi-agent systems. Read the complete AOP documentation

Documentation

Documentation is located here at: docs.swarms.world

Examples

Explore comprehensive examples and tutorials to learn how to use Swarms effectively.

Category	Example	Description	Link
Basic Examples	Basic Agent	Simple agent setup and usage	Basic Agent
Basic Examples	Agent with Tools	Using agents with various tools	Agent with Tools
Basic Examples	Agent with Structured Outputs	Working with structured data outputs	Structured Outputs
Basic Examples	Agent with MCP Integration	Model Context Protocol integration	MCP Integration
Basic Examples	Vision Processing	Agents with image processing capabilities	Vision Processing
Basic Examples	Multiple Images	Working with multiple images	Multiple Images
Basic Examples	Vision and Tools	Combining vision with tool usage	Vision and Tools
Basic Examples	Agent Streaming	Real-time agent output streaming	Agent Streaming
Basic Examples	Agent Output Types	Different output formats and types	Output Types
Basic Examples	Gradio Chat Interface	Building interactive chat interfaces	Gradio UI
Model Providers	Model Providers Overview	Complete guide to supported models	Model Providers
Model Providers	OpenAI	OpenAI model integration	OpenAI Examples
Model Providers	Anthropic	Claude model integration	Anthropic Examples
Model Providers	Groq	Groq model integration	Groq Examples
Model Providers	Cohere	Cohere model integration	Cohere Examples
Model Providers	DeepSeek	DeepSeek model integration	DeepSeek Examples
Model Providers	Ollama	Local Ollama model integration	Ollama Examples
Model Providers	OpenRouter	OpenRouter model integration	OpenRouter Examples
Model Providers	XAI	XAI model integration	XAI Examples
Model Providers	Llama4	Llama4 model integration	Llama4 Examples
Multi-Agent Architecture	HierarchicalSwarm	Hierarchical agent orchestration	HierarchicalSwarm Examples
Multi-Agent Architecture	Hybrid Hierarchical-Cluster Swarm	Advanced hierarchical patterns	HHCS Examples
Multi-Agent Architecture	GroupChat	Multi-agent conversations	GroupChat Examples
Multi-Agent Architecture	Sequential Workflow	Step-by-step agent workflows	Sequential Examples
Multi-Agent Architecture	SwarmRouter	Universal swarm orchestration	SwarmRouter Examples
Multi-Agent Architecture	MultiAgentRouter	Minimal router example	MultiAgentRouter Examples
Multi-Agent Architecture	ConcurrentWorkflow	Parallel agent execution	Concurrent Examples
Multi-Agent Architecture	Mixture of Agents	Expert agent collaboration	MoA Examples
Multi-Agent Architecture	Unique Swarms	Specialized swarm patterns	Unique Swarms
Multi-Agent Architecture	Agents as Tools	Using agents as tools in workflows	Agents as Tools
Multi-Agent Architecture	Aggregate Responses	Combining multiple agent outputs	Aggregate Examples
Multi-Agent Architecture	Interactive GroupChat	Real-time agent interactions	Interactive GroupChat
Deployment Solutions	Agent Orchestration Protocol (AOP)	Deploy agents as distributed services with discovery and management	AOP Reference
Applications	Advanced Research System	Multi-agent research system inspired by Anthropic's research methodology	AdvancedResearch
Applications	Hospital Simulation	Healthcare simulation system using multi-agent architecture	HospitalSim
Applications	Browser Agents	Web automation with agents	Browser Agents
Applications	Medical Analysis	Healthcare applications	Medical Examples
Applications	Finance Analysis	Financial applications	Finance Examples
Cookbook & Templates	Examples Overview	Complete examples directory	Examples Index
Cookbook & Templates	Cookbook Index	Curated example collection	Cookbook
Cookbook & Templates	Paper Implementations	Research paper implementations	Paper Implementations
Cookbook & Templates	Templates & Applications	Reusable templates	Templates

Contribute to Swarms

Swarms is an open-source, community-driven framework aiming to accelerate a fully autonomous world by providing robust infrastructure for deploying and orchestrating millions of agents. By contributing, you can help advance multi-agent AI, collaborate with passionate peers, shape the agent economy, and enhance your expertise.

Learn more about how you can make a meaningful impact in our Contributor's Guide.

How to Contribute

We've made it easy to start contributing. Here's how you can help:

Find an Issue to Tackle: The best way to begin is by visiting our contributing project board. Look for issues tagged with good first issue—these are specifically selected for new contributors.
Report a Bug or Request a Feature: Have a new idea or found something that isn't working right? We'd love to hear from you. Please file a Bug Report or Feature Request on our GitHub Issues page.
Understand Our Workflow and Standards: Before submitting your work, please review our complete Contribution Guidelines. To help maintain code quality, we also encourage you to read our guide on Code Cleanliness.
Join the Discussion: To participate in roadmap discussions and connect with other developers, join our community on Discord.

Thank You to Our Contributors

Thank you for contributing to swarms. Your work is extremely appreciated and recognized.

Join the Discord

Join thousands of agent builders and AI engineers in the Swarms Discord for technical support, project showcases, collaboration, and the latest swarms ecosystem updates.

Join the Swarms Discord →

Thank You to Our Community

We're incredibly grateful to everyone who supports Swarms! Your stars, forks, and contributions help make this project better every day.

Join the Swarms Community!

Join our community of agent engineers and researchers for technical support, cutting-edge updates, and exclusive access to world-class agent engineering insights!

Platform	Description	Link
Documentation	Official documentation and guides	docs.swarms.world
Blog	Latest updates and technical articles	Medium
Discord	Live chat and community support	Join Discord
Twitter	Latest news and announcements	@swarms_corp
LinkedIn	Professional network and updates	The Swarm Corporation
YouTube	Tutorials and demos	Swarms Channel
Events	Join our community events	Sign up here
Onboarding Session	Get onboarded with Kye Gomez, creator and lead maintainer of Swarms	Book Session

Citation

If you use swarms in your research, please cite the project by referencing the metadata in CITATION.cff.

@misc{SWARMS_2022,
  author  = {Kye Gomez and Pliny and Zack Bradshaw and Ilumn and Harshal and the Swarms Community},
  title   = {{Swarms: Production-Grade Multi-Agent Infrastructure Platform}},
  year    = {2022},
  howpublished = {\url{https://github.com/kyegomez/swarms}},
  note    = {Documentation available at \url{https://docs.swarms.world}},
  version = {latest}

License

Swarms is licensed under the Apache License 2.0. Learn more here

Features

Supported Protocols & Integrations

Install

Using pip

Using uv (Recommended)

Using poetry

From source

Environment Configuration

Your First Agent

Autonomous Agent with `max_loops="auto"`

Your First Swarm: Multi-Agent Collaboration

Available Multi-Agent Architectures

SequentialWorkflow

ConcurrentWorkflow

AgentRearrange

SwarmRouter: The Universal Swarm Orchestrator

AutoSwarmBuilder: Autonomous Agent Generation

MixtureOfAgents (MoA)

GroupChat

HierarchicalSwarm

HeavySwarm

Social Algorithms

Agent Orchestration Protocol (AOP)

Documentation

Examples

Contribute to Swarms

How to Contribute

Thank You to Our Contributors

Join the Discord

Thank You to Our Community

Join the Swarms Community!

Citation

License

关于 About

语言 Languages

提交活跃度 Commit Activity

核心贡献者 Contributors

Features

Supported Protocols & Integrations

Install

Using pip

Using uv (Recommended)

Using poetry

From source

Environment Configuration

Your First Agent

Autonomous Agent with max_loops="auto"

Your First Swarm: Multi-Agent Collaboration

Available Multi-Agent Architectures

SequentialWorkflow

ConcurrentWorkflow

AgentRearrange

SwarmRouter: The Universal Swarm Orchestrator

AutoSwarmBuilder: Autonomous Agent Generation

MixtureOfAgents (MoA)

GroupChat

HierarchicalSwarm

HeavySwarm

Social Algorithms

Agent Orchestration Protocol (AOP)

Documentation

Examples

Contribute to Swarms

How to Contribute

Thank You to Our Contributors

Join the Discord

Thank You to Our Community

Join the Swarms Community!

Citation

License

关于 About

语言 Languages

提交活跃度 Commit Activity

核心贡献者 Contributors

Autonomous Agent with `max_loops="auto"`