Swarms is a modular framework that enables reliable and useful multi-agent collaboration at scale to automate real-world tasks.

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Vision

At Swarms, we're transforming the landscape of AI from siloed AI agents to a unified 'swarm' of intelligence. Through relentless iteration and the power of collective insight from our 1500+ Agora researchers, we're developing a groundbreaking framework for AI collaboration. Our mission is to catalyze a paradigm shift, advancing Humanity with the power of unified autonomous AI agent swarms.

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🤝 Schedule a 1-on-1 Session

Book a 1-on-1 Session with Kye, the Creator, to discuss any issues, provide feedback, or explore how we can improve Swarms for you.

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Installation

pip3 install --upgrade swarms

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Usage

We have a small gallery of examples to run here, for more check out the docs to build your own agent and or swarms!

Flow Example

• The Flow is a superior iteratioin of the LLMChain from Langchain, our intent with Flow is to create the most reliable loop structure that gives the agents their "autonomy" through 3 main methods of interaction, one through user specified loops, then dynamic where the agent parses a token, and or an interactive human input verison, or a mix of all 3.

from swarms.models import OpenAIChat
from swarms.structs import Flow

api_key = ""


# Initialize the language model,
# This model can be swapped out with Anthropic, ETC, Huggingface Models like Mistral, ETC
llm = OpenAIChat(
    openai_api_key=api_key,
    temperature=0.5,
)

# Initialize the flow
flow = Flow(
    llm=llm,
    max_loops=5,
)

out = flow.run("Generate a 10,000 word blog, say Stop when done")
print(out)

GodMode

• A powerful tool for concurrent execution of tasks using multiple Language Model (LLM) instances.

from swarms.swarms import GodMode
from swarms.models import OpenAIChat

api_key = ""

llm = OpenAIChat(
    openai_api_key=api_key
)


llms = [
    llm,
    llm,
    llm
]

god_mode = GodMode(llms)

task = 'Generate a 10,000 word blog on health and wellness.'

out = god_mode.run(task)
god_mode.print_responses(task)

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SequentialWorkflow

• Execute tasks step by step by passing in an LLM and the task description!
• Pass in flows with various LLMs
• Save and restore Workflow states!

from swarms.models import OpenAIChat
from swarms.structs import Flow
from swarms.structs.sequential_workflow import SequentialWorkflow

# Example usage
api_key = (
    ""  # Your actual API key here
)

# Initialize the language flow
llm = OpenAIChat(
    openai_api_key=api_key,
    temperature=0.5,
    max_tokens=3000,
)

# Initialize the Flow with the language flow
flow1 = Flow(llm=llm, max_loops=1, dashboard=False)

# Create another Flow for a different task
flow2 = Flow(llm=llm, max_loops=1, dashboard=False)

# Create the workflow
workflow = SequentialWorkflow(max_loops=1)

# Add tasks to the workflow
workflow.add("Generate a 10,000 word blog on health and wellness.", flow1)

# Suppose the next task takes the output of the first task as input
workflow.add("Summarize the generated blog", flow2)

# Run the workflow
workflow.run()

# Output the results
for task in workflow.tasks:
    print(f"Task: {task.description}, Result: {task.result}")

OmniModalAgent

• OmniModal Agent is an LLM that access to 10+ multi-modal encoders and diffusers! It can generate images, videos, speech, music and so much more, get started with:

from swarms.models import OpenAIChat
from swarms.agents import OmniModalAgent

api_key = "SK-"

llm = OpenAIChat(model_name="gpt-4", openai_api_key=api_key)

agent = OmniModalAgent(llm)

agent.run("Create a video of a swarm of fish")

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Documentation

• For documentation, go here, swarms.apac.ai

Contribute

We're always looking for contributors to help us improve and expand this project. If you're interested, please check out our Contributing Guidelines and our contributing board

License

MIT