swarms 2.2.6

Swarms - Pytorch

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

• Reliable Structure that provides LLMS autonomy
• Extremely Customizeable with stopping conditions, interactivity, dynamical temperature, loop intervals, and so much more
• Enterprise Grade + Production Grade: Flow is designed and optimized for automating real-world tasks at scale!

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(
    # model_name="gpt-4"
    openai_api_key=api_key,
    temperature=0.5,
    # max_tokens=100,
)

## Initialize the workflow
flow = Flow(
    llm=llm,
    max_loops=2,
    dashboard=True,
    # stopping_condition=None,  # You can define a stopping condition as needed.
    # loop_interval=1,
    # retry_attempts=3,
    # retry_interval=1,
    # interactive=False,  # Set to 'True' for interactive mode.
    # dynamic_temperature=False,  # Set to 'True' for dynamic temperature handling.
)

# out = flow.load_state("flow_state.json")
# temp = flow.dynamic_temperature()
# filter = flow.add_response_filter("Trump")
out = flow.run("Generate a 10,000 word blog on health and wellness.")
# out = flow.validate_response(out)
# out = flow.analyze_feedback(out)
# out = flow.print_history_and_memory()
# # out = flow.save_state("flow_state.json")
# print(out)

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SequentialWorkflow

• A Sequential swarm of autonomous agents where each agent's outputs are fed into the next agent
• Save and Restore Workflow states!
• Integrate Flow's with various LLMs and Multi-Modality Models

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}")

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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

Community

• Join the Swarms community here on Discord!

License

MIT