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

🐦 Twitter • 📢 Discord • Swarms Website • 📙 Documentation • Swarms Marketplace

✨ Features

Category	Features	Benefits
🏢 Enterprise Architecture	• Production-Ready Infrastructure • High Reliability Systems • Modular Design • Comprehensive Logging	• Reduced downtime • Easier maintenance • Better debugging • Enhanced monitoring
🤖 Agent Orchestration	• Hierarchical Swarms • Parallel Processing • Sequential Workflows • Graph-based Workflows • Dynamic Agent Rearrangement	• Complex task handling • Improved performance • Flexible workflows • Optimized execution
🔄 Integration Capabilities	• Multi-Model Support • Custom Agent Creation • Extensive Tool Library • Multiple Memory Systems	• Provider flexibility • Custom solutions • Extended functionality • Enhanced memory management
📈 Scalability	• Concurrent Processing • Resource Management • Load Balancing • Horizontal Scaling	• Higher throughput • Efficient resource use • Better performance • Easy scaling
🛠️ Developer Tools	• Simple API • Extensive Documentation • Active Community • CLI Tools	• Faster development • Easy learning curve • Community support • Quick deployment
🔐 Security Features	• Error Handling • Rate Limiting • Monitoring Integration • Audit Logging	• Improved reliability • API protection • Better monitoring • Enhanced tracking
📊 Advanced Features	• SpreadsheetSwarm • Group Chat • Agent Registry • Mixture of Agents	• Mass agent management • Collaborative AI • Centralized control • Complex solutions
🔌 Provider Support	• OpenAI • Anthropic • ChromaDB • Custom Providers	• Provider flexibility • Storage options • Custom integration • Vendor independence
💪 Production Features	• Automatic Retries • Async Support • Environment Management • Type Safety	• Better reliability • Improved performance • Easy configuration • Safer code
🎯 Use Case Support	• Task-Specific Agents • Custom Workflows • Industry Solutions • Extensible Framework	• Quick deployment • Flexible solutions • Industry readiness • Easy customization

Guides and Walkthroughs

Refer to our documentation for production grade implementation details.

Section	Links
Installation	Installation
Quickstart	Get Started
Agent Internal Mechanisms	Agent Architecture
Agent API	Agent API
Integrating External Agents Griptape, Autogen, etc	Integrating External APIs
Creating Agents from YAML	Creating Agents from YAML
Why You Need Swarms	Why MultiAgent Collaboration is Necessary
Swarm Architectures Analysis	Swarm Architectures
Choosing the Right Swarm for Your Business Problem¶	CLICK HERE
AgentRearrange Docs	CLICK HERE

Install 💻

Using pip

$ pip3 install -U swarms

Using uv (Recommended)

uv is a fast Python package installer and resolver, written in Rust.

# Install uv
$ curl -LsSf https://astral.sh/uv/install.sh | sh

# Install swarms using uv
$ uv pip install swarms

Using poetry

# Install poetry if you haven't already
$ curl -sSL https://install.python-poetry.org | python3 -

# Add swarms to your project
$ poetry add swarms

From source

# Clone the repository
$ git clone https://github.com/kyegomez/swarms.git
$ cd swarms

# Install with pip
$ pip install -e .

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-4o-mini", # Specify the LLM
    max_loops=1,              # 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?")

🤝 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-4o-mini",
)

# 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-4o-mini",
)

# 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)

🏗️ Swarm Architectures for Production Workflows

swarms provides a variety of powerful, pre-built architectures to orchestrate agents in different 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; one agent's output is the next one's input.	Step-by-step processes like data transformation pipelines, report generation.
ConcurrentWorkflow	Agents run tasks simultaneously for maximum efficiency.	High-throughput tasks like batch processing, parallel data analysis.
AgentRearrange	Dynamically maps complex relationships (e.g., `a -> b, c`) between agents.	Flexible and adaptive workflows, task distribution, dynamic routing.
GraphWorkflow	Orchestrates agents as nodes in a Directed Acyclic Graph (DAG).	Complex projects with intricate dependencies, like software builds.
MixtureOfAgents (MoA)	Utilizes multiple expert agents in parallel and synthesizes their outputs.	Complex problem-solving, achieving state-of-the-art performance through collaboration.
GroupChat	Agents collaborate and make decisions through a conversational interface.	Real-time collaborative decision-making, negotiations, brainstorming.
ForestSwarm	Dynamically selects the most suitable agent or tree of agents for a given task.	Task routing, optimizing for expertise, complex decision-making trees.
SpreadSheetSwarm	Manages thousands of agents concurrently, tracking tasks and outputs in a structured format.	Massive-scale parallel operations, large-scale data generation and analysis.

SequentialWorkflow

A SequentialWorkflow executes tasks in a strict order, forming a pipeline where each agent builds upon the work of the previous one.

Description: Ideal for processes that have clear, ordered steps. This ensures that tasks with dependencies are handled correctly.

from swarms import Agent, SequentialWorkflow

# Initialize agents for a 3-step process
# 1. Generate an idea
idea_generator = Agent(agent_name="IdeaGenerator", system_prompt="Generate a unique startup idea.", model_name="gpt-4o-mini")
# 2. Validate the idea
validator = Agent(agent_name="Validator", system_prompt="Take this startup idea and analyze its market viability.", model_name="gpt-4o-mini")
# 3. Create a pitch
pitch_creator = Agent(agent_name="PitchCreator", system_prompt="Write a 3-sentence elevator pitch for this validated startup idea.", model_name="gpt-4o-mini")

# Create the sequential workflow
workflow = SequentialWorkflow(agents=[idea_generator, validator, pitch_creator])

# Run the workflow
elevator_pitch = workflow.run()
print(elevator_pitch)

ConcurrentWorkflow (with `SpreadSheetSwarm`)

A concurrent workflow runs multiple agents simultaneously. SpreadSheetSwarm is a powerful implementation that can manage thousands of concurrent agents and log their outputs to a CSV file.

Description: Use this for high-throughput tasks that can be performed in parallel, drastically reducing execution time.

from swarms import Agent, SpreadSheetSwarm

# Define a list of tasks (e.g., social media posts to generate)
platforms = ["Twitter", "LinkedIn", "Instagram"]

# Create an agent for each task
agents = [
    Agent(
        agent_name=f"{platform}-Marketer",
        system_prompt=f"Generate a real estate marketing post for {platform}.",
        model_name="gpt-4o-mini",
    )
    for platform in platforms
]

# Initialize the swarm to run these agents concurrently
swarm = SpreadSheetSwarm(
    agents=agents,
    autosave_on=True,
    save_file_path="marketing_posts.csv",
)

# Run the swarm with a single, shared task description
property_description = "A beautiful 3-bedroom house in sunny California."
swarm.run(task=f"Generate a post about: {property_description}")
# Check marketing_posts.csv for the results!

AgentRearrange

Inspired by einsum, AgentRearrange lets you define complex, non-linear relationships between agents using a simple string-based syntax. Learn more

Description: Perfect for orchestrating dynamic workflows where agents might work in parallel, sequence, or a combination of both.

from swarms import Agent, AgentRearrange

# Define agents
researcher = Agent(agent_name="researcher", model_name="gpt-4o-mini")
writer = Agent(agent_name="writer", model_name="gpt-4o-mini")
editor = Agent(agent_name="editor", model_name="gpt-4o-mini")

# 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 system
# The researcher will generate content, and then both the writer and editor
# will process that content in parallel.
outputs = rearrange_system.run("Analyze the impact of AI on modern cinema.")
print(outputs)

GraphWorkflow

GraphWorkflow orchestrates tasks using a Directed Acyclic Graph (DAG), allowing you to manage complex dependencies where some tasks must wait for others to complete.

Description: Essential for building sophisticated pipelines, like in software development or complex project management, where task order and dependencies are critical.

from swarms import Agent, GraphWorkflow, Node, Edge, NodeType

# Define agents and a simple python function as nodes
code_generator = Agent(agent_name="CodeGenerator", system_prompt="Write Python code for the given task.", model_name="gpt-4o-mini")
code_tester = Agent(agent_name="CodeTester", system_prompt="Test the given Python code and find bugs.", model_name="gpt-4o-mini")

# Create nodes for the graph
node1 = Node(id="generator", agent=code_generator)
node2 = Node(id="tester", agent=code_tester)

# Create the graph and define the dependency
graph = GraphWorkflow()
graph.add_nodes([node1, node2])
graph.add_edge(Edge(source="generator", target="tester")) # Tester runs after generator

# Set entry and end points
graph.set_entry_points(["generator"])
graph.set_end_points(["tester"])

# Run the graph workflow
results = graph.run("Create a function that calculates the factorial of a number.")
print(results)

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.

Description: Use this to achieve state-of-the-art performance on complex reasoning tasks by leveraging the collective intelligence of specialized agents.

from swarms import Agent, MixtureOfAgents

# Define expert agents
financial_analyst = Agent(agent_name="FinancialAnalyst", system_prompt="Analyze financial data.", model_name="gpt-4o-mini")
market_analyst = Agent(agent_name="MarketAnalyst", system_prompt="Analyze market trends.", model_name="gpt-4o-mini")
risk_analyst = Agent(agent_name="RiskAnalyst", system_prompt="Analyze investment risks.", model_name="gpt-4o-mini")

# 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-4o-mini"
)

# 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.

Description: 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-4o-mini")
tech_critic = Agent(agent_name="TechCritic", system_prompt="Argue against the unchecked advancement of AI.", model_name="gpt-4o-mini")

# 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']}")

Onboarding Session

Get onboarded now with the creator and lead maintainer of Swarms, Kye Gomez, who will show you how to get started with the installation, usage examples, and starting to build your custom use case! CLICK HERE

Documentation

Documentation is located here at: docs.swarms.world

🫶 Contributions:

The easiest way to contribute is to pick any issue with the good first issue tag 💪. Read the Contributing guidelines here. Bug Report? File here | Feature Request? File here

Swarms is an open-source project, and contributions are VERY welcome. If you want to contribute, you can create new features, fix bugs, or improve the infrastructure. Please refer to the CONTRIBUTING.md and our contributing board to participate in Roadmap discussions!

Connect With Us

Platform	Link	Description
📚 Documentation	docs.swarms.world	Official documentation and guides
📝 Blog	Medium	Latest updates and technical articles
💬 Discord	Join Discord	Live chat and community support
🐦 Twitter	@kyegomez	Latest news and announcements
👥 LinkedIn	The Swarm Corporation	Professional network and updates
📺 YouTube	Swarms Channel	Tutorials and demos
🎫 Events	Sign up here	Join our community events

Citation

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

@misc{SWARMS_2022,
  author  = {Gomez, Kye and Pliny and More, Harshal and 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

APACHE

23 KiB Raw Blame History