Usage Examples 🤖

Multi-Agent Orchestration is incredibly painful swarms is making it simple, seamless, and reliable.

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Swarms is an enterprise grade and production ready multi-agent collaboration framework that enables you to orchestrate many agents to work collaboratively at scale to automate real-world activities.

Feature	Description	Performance Impact	Documentation Link
Models	Pre-trained models that can be utilized for various tasks within the swarm framework.	⭐⭐⭐	Documentation
Models APIs	APIs to interact with and utilize the models effectively, providing interfaces for inference, training, and fine-tuning.	⭐⭐⭐	Documentation
Agents with Tools	Agents equipped with specialized tools to perform specific tasks more efficiently, such as data processing, analysis, or interaction with external systems.	⭐⭐⭐⭐	Documentation
Agents with Memory	Mechanisms for agents to store and recall past interactions, improving learning and adaptability over time.	⭐⭐⭐⭐	Documentation
Multi-Agent Orchestration	Coordination of multiple agents to work together seamlessly on complex tasks, leveraging their individual strengths to achieve higher overall performance.	⭐⭐⭐⭐⭐	Documentation

The performance impact is rated on a scale from one to five stars, with multi-agent orchestration being the highest due to its ability to combine the strengths of multiple agents and optimize task execution.

Install 💻

$ pip3 install -U swarms

Usage Examples 🤖

Google Collab Example

Run example in Collab:

`Agents`

A fully plug-and-play autonomous agent powered by an LLM extended by a long-term memory database, and equipped with function calling for tool usage! By passing in an LLM, you can create a fully autonomous agent with extreme customization and reliability, ready for real-world task automation!

Features:

✅ Any LLM / Any framework

✅ Extremely customize-able with max loops, autosaving, import docs (PDFS, TXT, CSVs, etc), tool usage, etc etc

✅ Long term memory database with RAG (ChromaDB, Pinecone, Qdrant)

import os

from dotenv import load_dotenv

# Import the OpenAIChat model and the Agent struct
from swarms import Agent, OpenAIChat

# Load the environment variables
load_dotenv()

# Get the API key from the environment
api_key = os.environ.get("OPENAI_API_KEY")

# Initialize the language model
llm = OpenAIChat(
    temperature=0.5, openai_api_key=api_key, max_tokens=4000
)


## Initialize the workflow
agent = Agent(llm=llm, max_loops=1, autosave=True, dashboard=True)

# Run the workflow on a task
agent.run("Generate a 10,000 word blog on health and wellness.")

`Agent` + Long Term Memory

Agent equipped with quasi-infinite long term memory. Great for long document understanding, analysis, and retrieval.

from swarms import Agent, OpenAIChat
from playground.memory.chromadb_example import ChromaDB # Copy and paste the code and put it in your own local directory.

# Making an instance of the ChromaDB class
memory = ChromaDB(
    metric="cosine",
    n_results=3,
    output_dir="results",
    docs_folder="docs",
)

# Initializing the agent with the Gemini instance and other parameters
agent = Agent(
    agent_name="Covid-19-Chat",
    agent_description=(
        "This agent provides information about COVID-19 symptoms."
    ),
    llm=OpenAIChat(),
    max_loops="auto",
    autosave=True,
    verbose=True,
    long_term_memory=memory,
    stopping_condition="finish",
)

# Defining the task and image path
task = ("What are the symptoms of COVID-19?",)

# Running the agent with the specified task and image
out = agent.run(task)
print(out)

`Agent` ++ Long Term Memory ++ Tools!

An LLM equipped with long term memory and tools, a full stack agent capable of automating all and any digital tasks given a good prompt.

from swarms import Agent, ChromaDB, OpenAIChat

# Making an instance of the ChromaDB class
memory = ChromaDB(
    metric="cosine",
    n_results=3,
    output_dir="results",
    docs_folder="docs",
)

# Initialize a tool
def search_api(query: str):
    # Add your logic here
    return query

# Initializing the agent with the Gemini instance and other parameters
agent = Agent(
    agent_name="Covid-19-Chat",
    agent_description=(
        "This agent provides information about COVID-19 symptoms."
    ),
    llm=OpenAIChat(),
    max_loops="auto",
    autosave=True,
    verbose=True,
    long_term_memory=memory,
    stopping_condition="finish",
    tools=[search_api],
)

# Defining the task and image path
task = ("What are the symptoms of COVID-19?",)

# Running the agent with the specified task and image
out = agent.run(task)
print(out)

Devin

Implementation of Devin in less than 90 lines of code with several tools: terminal, browser, and edit files.

from swarms import Agent, Anthropic
import subprocess

# Model
llm = Anthropic(
    temperature=0.1,
)

# Tools
def terminal(
    code: str,
):
    """
    Run code in the terminal.

    Args:
        code (str): The code to run in the terminal.

    Returns:
        str: The output of the code.
    """
    out = subprocess.run(
        code, shell=True, capture_output=True, text=True
    ).stdout
    return str(out)

def browser(query: str):
    """
    Search the query in the browser with the `browser` tool.

    Args:
        query (str): The query to search in the browser.

    Returns:
        str: The search results.
    """
    import webbrowser

    url = f"https://www.google.com/search?q={query}"
    webbrowser.open(url)
    return f"Searching for {query} in the browser."

def create_file(file_path: str, content: str):
    """
    Create a file using the file editor tool.

    Args:
        file_path (str): The path to the file.
        content (str): The content to write to the file.

    Returns:
        str: The result of the file creation operation.
    """
    with open(file_path, "w") as file:
        file.write(content)
    return f"File {file_path} created successfully."

def file_editor(file_path: str, mode: str, content: str):
    """
    Edit a file using the file editor tool.

    Args:
        file_path (str): The path to the file.
        mode (str): The mode to open the file in.
        content (str): The content to write to the file.

    Returns:
        str: The result of the file editing operation.
    """
    with open(file_path, mode) as file:
        file.write(content)
    return f"File {file_path} edited successfully."


# Agent
agent = Agent(
    agent_name="Devin",
    system_prompt=(
        "Autonomous agent that can interact with humans and other"
        " agents. Be Helpful and Kind. Use the tools provided to"
        " assist the user. Return all code in markdown format."
    ),
    llm=llm,
    max_loops="auto",
    autosave=True,
    dashboard=False,
    streaming_on=True,
    verbose=True,
    stopping_token="<DONE>",
    interactive=True,
    tools=[terminal, browser, file_editor, create_file],
    code_interpreter=True,
    # streaming=True,
)

# Run the agent
out = agent("Create a new file for a plan to take over the world.")
print(out)

`Agent`with Pydantic BaseModel as Output Type

The following is an example of an agent that intakes a pydantic basemodel and outputs it at the same time:

from pydantic import BaseModel, Field
from swarms import Anthropic, Agent


# Initialize the schema for the person's information
class Schema(BaseModel):
    name: str = Field(..., title="Name of the person")
    agent: int = Field(..., title="Age of the person")
    is_student: bool = Field(..., title="Whether the person is a student")
    courses: list[str] = Field(
        ..., title="List of courses the person is taking"
    )


# Convert the schema to a JSON string
tool_schema = Schema(
    name="Tool Name",
    agent=1,
    is_student=True,
    courses=["Course1", "Course2"],
)

# Define the task to generate a person's information
task = "Generate a person's information based on the following schema:"

# Initialize the agent
agent = Agent(
    agent_name="Person Information Generator",
    system_prompt=(
        "Generate a person's information based on the following schema:"
    ),
    # Set the tool schema to the JSON string -- this is the key difference
    tool_schema=tool_schema,
    llm=Anthropic(),
    max_loops=3,
    autosave=True,
    dashboard=False,
    streaming_on=True,
    verbose=True,
    interactive=True,
    # Set the output type to the tool schema which is a BaseModel
    output_type=tool_schema,  # or dict, or str
    metadata_output_type="json",
    # List of schemas that the agent can handle
    list_tool_schemas=[tool_schema],
    function_calling_format_type="OpenAI",
    function_calling_type="json",  # or soon yaml
)

# Run the agent to generate the person's information
generated_data = agent.run(task)

# Print the generated data
print(f"Generated data: {generated_data}")

Multi Modal Autonomous Agent

Run the agent with multiple modalities useful for various real-world tasks in manufacturing, logistics, and health.

# Description: This is an example of how to use the Agent class to run a multi-modal workflow
import os

from dotenv import load_dotenv

from swarms.models.gpt4_vision_api import GPT4VisionAPI
from swarms.structs import Agent

# Load the environment variables
load_dotenv()

# Get the API key from the environment
api_key = os.environ.get("OPENAI_API_KEY")

# Initialize the language model
llm = GPT4VisionAPI(
    openai_api_key=api_key,
    max_tokens=500,
)

# Initialize the task
task = (
    "Analyze this image of an assembly line and identify any issues such as"
    " misaligned parts, defects, or deviations from the standard assembly"
    " process. IF there is anything unsafe in the image, explain why it is"
    " unsafe and how it could be improved."
)
img = "assembly_line.jpg"

## Initialize the workflow
agent = Agent(
    llm=llm, max_loops="auto", autosave=True, dashboard=True, multi_modal=True
)

# Run the workflow on a task
agent.run(task=task, img=img)

`ToolAgent`

ToolAgent is an agent that can use tools through JSON function calling. It intakes any open source model from huggingface and is extremely modular and plug in and play. We need help adding general support to all models soon.

from pydantic import BaseModel, Field
from transformers import AutoModelForCausalLM, AutoTokenizer

from swarms import ToolAgent
from swarms.utils.json_utils import base_model_to_json

# Load the pre-trained model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "databricks/dolly-v2-12b",
    load_in_4bit=True,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-12b")


# Initialize the schema for the person's information
class Schema(BaseModel):
    name: str = Field(..., title="Name of the person")
    agent: int = Field(..., title="Age of the person")
    is_student: bool = Field(
        ..., title="Whether the person is a student"
    )
    courses: list[str] = Field(
        ..., title="List of courses the person is taking"
    )


# Convert the schema to a JSON string
tool_schema = base_model_to_json(Schema)

# Define the task to generate a person's information
task = (
    "Generate a person's information based on the following schema:"
)

# Create an instance of the ToolAgent class
agent = ToolAgent(
    name="dolly-function-agent",
    description="Ana gent to create a child data",
    model=model,
    tokenizer=tokenizer,
    json_schema=tool_schema,
)

# Run the agent to generate the person's information
generated_data = agent.run(task)

# Print the generated data
print(f"Generated data: {generated_data}")

`Task`

For deeper control of your agent stack, Task is a simple structure for task execution with the Agent. Imagine zapier like LLM-based workflow automation.

✅ Task is a structure for task execution with the Agent.

✅ Tasks can have descriptions, scheduling, triggers, actions, conditions, dependencies, priority, and a history.

✅ The Task structure allows for efficient workflow automation with LLM-based agents.

import os

from dotenv import load_dotenv

from swarms.structs import Agent, OpenAIChat, Task

# Load the environment variables
load_dotenv()


# Define a function to be used as the action
def my_action():
    print("Action executed")


# Define a function to be used as the condition
def my_condition():
    print("Condition checked")
    return True


# Create an agent
agent = Agent(
    llm=OpenAIChat(openai_api_key=os.environ["OPENAI_API_KEY"]),
    max_loops=1,
    dashboard=False,
)

# Create a task
task = Task(
    description=(
        "Generate a report on the top 3 biggest expenses for small"
        " businesses and how businesses can save 20%"
    ),
    agent=agent,
)

# Set the action and condition
task.set_action(my_action)
task.set_condition(my_condition)

# Execute the task
print("Executing task...")
task.run()

# Check if the task is completed
if task.is_completed():
    print("Task completed")
else:
    print("Task not completed")

# Output the result of the task
print(f"Task result: {task.result}")

Multi-Agent Orchestration:

Swarms was designed to facilitate the communication between many different and specialized agents from a vast array of other frameworks such as langchain, autogen, crew, and more.

In traditional swarm theory, there are many types of swarms usually for very specialized use-cases and problem sets. Such as Hiearchical and sequential are great for accounting and sales, because there is usually a boss coordinator agent that distributes a workload to other specialized agents.

Name	Description	Code Link	Use Cases
Hierarchical Swarms	A system where agents are organized in a hierarchy, with higher-level agents coordinating lower-level agents to achieve complex tasks.	Code Link	Manufacturing process optimization, multi-level sales management, healthcare resource coordination
Agent Rearrange	A setup where agents rearrange themselves dynamically based on the task requirements and environmental conditions.	Code Link	Adaptive manufacturing lines, dynamic sales territory realignment, flexible healthcare staffing
Concurrent Workflows	Agents perform different tasks simultaneously, coordinating to complete a larger goal.	Code Link	Concurrent production lines, parallel sales operations, simultaneous patient care processes
Sequential Coordination	Agents perform tasks in a specific sequence, where the completion of one task triggers the start of the next.	Code Link	Step-by-step assembly lines, sequential sales processes, stepwise patient treatment workflows
Parallel Processing	Agents work on different parts of a task simultaneously to speed up the overall process.	Code Link	Parallel data processing in manufacturing, simultaneous sales analytics, concurrent medical tests

`SequentialWorkflow`

Sequential Workflow enables you to sequentially execute tasks with Agent and then pass the output into the next agent and onwards until you have specified your max loops. SequentialWorkflow is wonderful for real-world business tasks like sending emails, summarizing documents, and analyzing data.

✅ Save and Restore Workflow states!

✅ Multi-Modal Support for Visual Chaining

✅ Utilizes Agent class

from swarms import Agent, SequentialWorkflow, Anthropic


# Initialize the language model agent (e.g., GPT-3)
llm = Anthropic()

# Initialize agents for individual tasks
agent1 = Agent(
    agent_name="Blog generator",
    system_prompt="Generate a blog post like stephen king",
    llm=llm,
    max_loops=1,
    dashboard=False,
    tools=[],
)
agent2 = Agent(
    agent_name="summarizer",
    system_prompt="Sumamrize the blog post",
    llm=llm,
    max_loops=1,
    dashboard=False,
    tools=[],
)

# Create the Sequential workflow
workflow = SequentialWorkflow(
    agents=[agent1, agent2], max_loops=1, verbose=False
)

# Run the workflow
workflow.run(
    "Generate a blog post on how swarms of agents can help businesses grow."
)

`ConcurrentWorkflow`

ConcurrentWorkflow runs all the tasks all at the same time with the inputs you give it!

import os

from dotenv import load_dotenv

from swarms import Agent, ConcurrentWorkflow, OpenAIChat, Task

# Load environment variables from .env file
load_dotenv()

# Load environment variables
llm = OpenAIChat(openai_api_key=os.getenv("OPENAI_API_KEY"))
agent = Agent(llm=llm, max_loops=1)

# Create a workflow
workflow = ConcurrentWorkflow(max_workers=5)

# Create tasks
task1 = Task(agent, "What's the weather in miami")
task2 = Task(agent, "What's the weather in new york")
task3 = Task(agent, "What's the weather in london")

# Add tasks to the workflow
workflow.add(tasks=[task1, task2, task3])

# Run the workflow
workflow.run()

`RecursiveWorkflow`

RecursiveWorkflow will keep executing the tasks until a specific token like is located inside the text!

import os

from dotenv import load_dotenv

from swarms import Agent, OpenAIChat, RecursiveWorkflow, Task

# Load environment variables from .env file
load_dotenv()

# Load environment variables
llm = OpenAIChat(openai_api_key=os.getenv("OPENAI_API_KEY"))
agent = Agent(llm=llm, max_loops=1)

# Create a workflow
workflow = RecursiveWorkflow(stop_token="<DONE>")

# Create tasks
task1 = Task(agent, "What's the weather in miami")
task2 = Task(agent, "What's the weather in new york")
task3 = Task(agent, "What's the weather in london")

# Add tasks to the workflow
workflow.add(task1)
workflow.add(task2)
workflow.add(task3)

# Run the workflow
workflow.run()

`SwarmNetwork`

SwarmNetwork provides the infrasturcture for building extremely dense and complex multi-agent applications that span across various types of agents.

✅ Efficient Task Management: SwarmNetwork's intelligent agent pool and task queue management system ensures tasks are distributed evenly across agents. This leads to efficient use of resources and faster task completion.

✅ Scalability: SwarmNetwork can dynamically scale the number of agents based on the number of pending tasks. This means it can handle an increase in workload by adding more agents, and conserve resources when the workload is low by reducing the number of agents.

✅ Versatile Deployment Options: With SwarmNetwork, each agent can be run on its own thread, process, container, machine, or even cluster. This provides a high degree of flexibility and allows for deployment that best suits the user's needs and infrastructure.

import os

from dotenv import load_dotenv

# Import the OpenAIChat model and the Agent struct
from swarms import Agent, OpenAIChat, SwarmNetwork

# Load the environment variables
load_dotenv()

# Get the API key from the environment
api_key = os.environ.get("OPENAI_API_KEY")

# Initialize the language model
llm = OpenAIChat(
    temperature=0.5,
    openai_api_key=api_key,
)

## Initialize the workflow
agent = Agent(llm=llm, max_loops=1, agent_name="Social Media Manager")
agent2 = Agent(llm=llm, max_loops=1, agent_name=" Product Manager")
agent3 = Agent(llm=llm, max_loops=1, agent_name="SEO Manager")


# Load the swarmnet with the agents
swarmnet = SwarmNetwork(
    agents=[agent, agent2, agent3],
)

# List the agents in the swarm network
out = swarmnet.list_agents()
print(out)

# Run the workflow on a task
out = swarmnet.run_single_agent(
    agent2.id, "Generate a 10,000 word blog on health and wellness."
)
print(out)


# Run all the agents in the swarm network on a task
out = swarmnet.run_many_agents("Generate a 10,000 word blog on health and wellness.")
print(out)

Majority Voting

Multiple-agents will evaluate an idea based off of an parsing or evaluation function. From papers like "More agents is all you need

from swarms import Agent, MajorityVoting, ChromaDB, Anthropic

# Initialize the llm
llm = Anthropic()

# Agents
agent1 = Agent(
    llm = llm,
    system_prompt="You are the leader of the Progressive Party. What is your stance on healthcare?",
    agent_name="Progressive Leader",
    agent_description="Leader of the Progressive Party",
    long_term_memory=ChromaDB(),
    max_steps=1,
)

agent2 = Agent(
    llm=llm,
    agent_name="Conservative Leader",
    agent_description="Leader of the Conservative Party",
    long_term_memory=ChromaDB(),
    max_steps=1,
)

agent3 = Agent(
    llm=llm,
    agent_name="Libertarian Leader",
    agent_description="Leader of the Libertarian Party",
    long_term_memory=ChromaDB(),
    max_steps=1,
)

# Initialize the majority voting
mv = MajorityVoting(
    agents=[agent1, agent2, agent3],
    output_parser=llm.majority_voting,
    autosave=False,
    verbose=True,
)


# Start the majority voting
mv.run("What is your stance on healthcare?")

Real-World Deployment

Multi-Agent Swarm for Logistics

Here's a production grade swarm ready for real-world deployment in a factory and logistics settings like warehouses. This swarm can automate 3 costly and inefficient workflows, safety checks, productivity checks, and warehouse security.

import os

from dotenv import load_dotenv

from swarms.models import GPT4VisionAPI
from swarms.prompts.logistics import (
    Efficiency_Agent_Prompt,
    Health_Security_Agent_Prompt,
    Productivity_Agent_Prompt,
    Quality_Control_Agent_Prompt,
    Safety_Agent_Prompt,
    Security_Agent_Prompt,
    Sustainability_Agent_Prompt,
)
from swarms.structs import Agent

# Load ENV
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")

# GPT4VisionAPI
llm = GPT4VisionAPI(openai_api_key=api_key)

# Image for analysis
factory_image = "factory_image1.jpg"

# Initialize agents with respective prompts
health_security_agent = Agent(
    llm=llm,
    sop=Health_Security_Agent_Prompt,
    max_loops=1,
    multi_modal=True,
)

# Quality control agent
quality_control_agent = Agent(
    llm=llm,
    sop=Quality_Control_Agent_Prompt,
    max_loops=1,
    multi_modal=True,
)


# Productivity Agent
productivity_agent = Agent(
    llm=llm,
    sop=Productivity_Agent_Prompt,
    max_loops=1,
    multi_modal=True,
)

# Initiailize safety agent
safety_agent = Agent(llm=llm, sop=Safety_Agent_Prompt, max_loops=1, multi_modal=True)

# Init the security agent
security_agent = Agent(
    llm=llm, sop=Security_Agent_Prompt, max_loops=1, multi_modal=True
)


# Initialize sustainability agent
sustainability_agent = Agent(
    llm=llm,
    sop=Sustainability_Agent_Prompt,
    max_loops=1,
    multi_modal=True,
)


# Initialize efficincy agent
efficiency_agent = Agent(
    llm=llm,
    sop=Efficiency_Agent_Prompt,
    max_loops=1,
    multi_modal=True,
)

# Run agents with respective tasks on the same image
health_analysis = health_security_agent.run(
    "Analyze the safety of this factory", factory_image
)
quality_analysis = quality_control_agent.run(
    "Examine product quality in the factory", factory_image
)
productivity_analysis = productivity_agent.run(
    "Evaluate factory productivity", factory_image
)
safety_analysis = safety_agent.run(
    "Inspect the factory's adherence to safety standards",
    factory_image,
)
security_analysis = security_agent.run(
    "Assess the factory's security measures and systems",
    factory_image,
)
sustainability_analysis = sustainability_agent.run(
    "Examine the factory's sustainability practices", factory_image
)
efficiency_analysis = efficiency_agent.run(
    "Analyze the efficiency of the factory's manufacturing process",
    factory_image,
)

Build your own LLMs, Agents, and Swarms!

Swarms Compliant Model Interface

from swarms import BaseLLM

class vLLMLM(BaseLLM):
    def __init__(self, model_name='default_model', tensor_parallel_size=1, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.model_name = model_name
        self.tensor_parallel_size = tensor_parallel_size
        # Add any additional initialization here
    
    def run(self, task: str):
        pass

# Example
model = vLLMLM("mistral")

# Run the model
out = model("Analyze these financial documents and summarize of them")
print(out)

Swarms Compliant Agent Interface

from swarms import Agent


class MyCustomAgent(Agent):

    def __init__(self, *args, **kwargs):

        super().__init__(*args, **kwargs)

        # Custom initialization logic

    def custom_method(self, *args, **kwargs):

        # Implement custom logic here

        pass

    def run(self, task, *args, **kwargs):

        # Customize the run method

        response = super().run(task, *args, **kwargs)

        # Additional custom logic

        return response`

# Model
agent = MyCustomAgent()

# Run the agent
out = agent("Analyze and summarize these financial documents: ")
print(out)

Compliant Interface for Multi-Agent Collaboration

from swarms import AutoSwarm, AutoSwarmRouter, BaseSwarm


# Build your own Swarm
class MySwarm(BaseSwarm):
    def __init__(self, name="kyegomez/myswarm", *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.name = name

    def run(self, task: str, *args, **kwargs):
        # Add your multi-agent logic here
        # agent 1
        # agent 2
        # agent 3
        return "output of the swarm"


# Add your custom swarm to the AutoSwarmRouter
router = AutoSwarmRouter(
    swarms=[MySwarm]
)


# Create an AutoSwarm instance
autoswarm = AutoSwarm(
    name="kyegomez/myswarm",
    description="A simple API to build and run swarms",
    verbose=True,
    router=router,
)


# Run the AutoSwarm
autoswarm.run("Analyze these financial data and give me a summary")

`AgentRearrange`

Inspired by Einops and einsum, this orchestration techniques enables you to map out the relationships between various agents. For example you specify linear and sequential relationships like a -> a1 -> a2 -> a3 or concurrent relationships where the first agent will send a message to 3 agents all at once: a -> a1, a2, a3. You can customize your workflow to mix sequential and concurrent relationships. Docs Available:

from swarms import Agent, AgentRearrange, rearrange, Anthropic


# Initialize the director agent

director = Agent(
    agent_name="Director",
    system_prompt="Directs the tasks for the workers",
    llm=Anthropic(),
    max_loops=1,
    dashboard=False,
    streaming_on=True,
    verbose=True,
    stopping_token="<DONE>",
    state_save_file_type="json",
    saved_state_path="director.json",
)


# Initialize worker 1

worker1 = Agent(
    agent_name="Worker1",
    system_prompt="Generates a transcript for a youtube video on what swarms are",
    llm=Anthropic(),
    max_loops=1,
    dashboard=False,
    streaming_on=True,
    verbose=True,
    stopping_token="<DONE>",
    state_save_file_type="json",
    saved_state_path="worker1.json",
)


# Initialize worker 2
worker2 = Agent(
    agent_name="Worker2",
    system_prompt="Summarizes the transcript generated by Worker1",
    llm=Anthropic(),
    max_loops=1,
    dashboard=False,
    streaming_on=True,
    verbose=True,
    stopping_token="<DONE>",
    state_save_file_type="json",
    saved_state_path="worker2.json",
)


# Create a list of agents
agents = [director, worker1, worker2]

# Define the flow pattern
flow = "Director -> Worker1 -> Worker2"

# Using AgentRearrange class
agent_system = AgentRearrange(agents=agents, flow=flow)
output = agent_system.run(
    "Create a format to express and communicate swarms of llms in a structured manner for youtube"
)
print(output)


# Using rearrange function
output = rearrange(
    agents,
    flow,
    "Create a format to express and communicate swarms of llms in a structured manner for youtube",
)

print(output)

`HierarhicalSwarm`

Coming soon...

`AgentLoadBalancer`

Coming soon...

`GraphSwarm`

Coming soon...

`MixtureOfAgents`

This is an implementation from the paper: "Mixture-of-Agents Enhances Large Language Model Capabilities" by together.ai, it achieves SOTA on AlpacaEval 2.0, MT-Bench and FLASK, surpassing GPT-4 Omni. Great for tasks that need to be parallelized and then sequentially fed into another loop

from swarms import Agent, OpenAIChat
from swarms.structs.mixture_of_agents import MixtureOfAgents

# Initialize the director agent
director = Agent(
    agent_name="Director",
    system_prompt="Directs the tasks for the accountants",
    llm=OpenAIChat(),
    max_loops=1,
    dashboard=False,
    streaming_on=True,
    verbose=True,
    stopping_token="<DONE>",
    state_save_file_type="json",
    saved_state_path="director.json",
)

# Initialize accountant 1
accountant1 = Agent(
    agent_name="Accountant1",
    system_prompt="Prepares financial statements",
    llm=OpenAIChat(),
    max_loops=1,
    dashboard=False,
    streaming_on=True,
    verbose=True,
    stopping_token="<DONE>",
    state_save_file_type="json",
    saved_state_path="accountant1.json",
)

# Initialize accountant 2
accountant2 = Agent(
    agent_name="Accountant2",
    system_prompt="Audits financial records",
    llm=OpenAIChat(),
    max_loops=1,
    dashboard=False,
    streaming_on=True,
    verbose=True,
    stopping_token="<DONE>",
    state_save_file_type="json",
    saved_state_path="accountant2.json",
)

# Create a list of agents
agents = [director, accountant1, accountant2]


# Swarm
swarm = MixtureOfAgents(
    name="Mixture of Accountants",
    agents=agents,
    layers=3,
    final_agent=director,
)


# Run the swarm
out = swarm.run("Prepare financial statements and audit financial records")
print(out)

Documentation

Documentation is located here at: swarms.apac.ai

Folder Structure

The swarms package has been meticlously crafted for extreme use-ability and understanding, the swarms package is split up into various modules such as swarms.agents that holds pre-built agents, swarms.structs that holds a vast array of structures like Agent and multi agent structures. The 3 most important are structs, models, and agents.

├── __init__.py
├── agents
├── artifacts
├── memory
├── schemas
├── models
├── prompts
├── structs
├── telemetry
├── tools
├── utils
└── workers

🫶 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!

Community

Join our growing community around the world, for real-time support, ideas, and discussions on Swarms 😊

View our official Blog
Chat live with us on Discord
Follow us on Twitter
Connect with us on LinkedIn
Visit us on YouTube
Join the Swarms community on Discord!
Join our Swarms Community Gathering every Thursday at 1pm NYC Time to unlock the potential of autonomous agents in automating your daily tasks Sign up here

Discovery Call

Book a discovery call to learn how Swarms can lower your operating costs by 40% with swarms of autonomous agents in lightspeed. Click here to book a time that works for you!

Accelerate Backlog

Accelerate Bugs, Features, and Demos to implement by supporting us here:

Docker Instructions

Learn More Here About Deployments In Docker

Swarm Newsletter 🤖 🤖 🤖 📧

Sign up to the Swarm newsletter to receive updates on the latest Autonomous agent research papers, step by step guides on creating multi-agent app, and much more Swarmie goodiness 😊

CLICK HERE TO SIGNUP

License

Apache License

Citations

Please cite Swarms in your paper or your project if you found it beneficial in any way! Appreciate you.

@misc{swarms,
  author = {Gomez, Kye},
  title = {{Swarms: The Multi-Agent Collaboration Framework}},
  howpublished = {\url{https://github.com/kyegomez/swarms}},
  year = {2023},
  note = {Accessed: Date}
}

@misc{wang2024mixtureofagents,
    title={Mixture-of-Agents Enhances Large Language Model Capabilities}, 
    author={Junlin Wang and Jue Wang and Ben Athiwaratkun and Ce Zhang and James Zou},
    year={2024},
    eprint={2406.04692},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

Name		Name	Last commit message	Last commit date
Latest commit History 2,746 Commits
.github		.github
docs		docs
docs_edit		docs_edit
images		images
paru		paru
playground		playground
scripts		scripts
servers		servers
swarms		swarms
tests		tests
.env.example		.env.example
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
SECURITY.md		SECURITY.md
example.py		example.py
first_thing.py		first_thing.py
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt
startup.py		startup.py

License

diegocabello/swarms

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Install 💻

Usage Examples 🤖

Google Collab Example

Agents

Agent + Long Term Memory

Agent ++ Long Term Memory ++ Tools!

Devin

Agentwith Pydantic BaseModel as Output Type

Multi Modal Autonomous Agent

ToolAgent

Task

Multi-Agent Orchestration:

SequentialWorkflow

ConcurrentWorkflow

RecursiveWorkflow

SwarmNetwork

Majority Voting

Real-World Deployment

Multi-Agent Swarm for Logistics

Build your own LLMs, Agents, and Swarms!

Swarms Compliant Model Interface

Swarms Compliant Agent Interface

Compliant Interface for Multi-Agent Collaboration

AgentRearrange

HierarhicalSwarm

AgentLoadBalancer

GraphSwarm

MixtureOfAgents

Documentation

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🫶 Contributions:

Community

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`Agents`

`Agent` + Long Term Memory

`Agent` ++ Long Term Memory ++ Tools!

`Agent`with Pydantic BaseModel as Output Type

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`SequentialWorkflow`

`ConcurrentWorkflow`

`RecursiveWorkflow`

`SwarmNetwork`

`AgentRearrange`

`HierarhicalSwarm`

`AgentLoadBalancer`

`GraphSwarm`

`MixtureOfAgents`

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