swarms 1.9.2

Swarms - Pytorch

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

Share on Social Media

Purpose

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.

---

🤝 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.

---

Installation

pip3 install --upgrade swarms

---

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!

MultiAgentDebate

• MultiAgentDebate is a simple class that enables multi agent collaboration.

from swarms.workers import Worker
from swarms.swarms import MultiAgentDebate, select_speaker
from swarms.models import OpenAIChat


api_key = "sk-"

llm = OpenAIChat(
    model_name='gpt-4', 
    openai_api_key=api_key, 
    temperature=0.5
)

node = Worker(
    llm=llm,
    openai_api_key=api_key,
    ai_name="Optimus Prime",
    ai_role="Worker in a swarm",
    external_tools = None,
    human_in_the_loop = False,
    temperature = 0.5,
)

node2 = Worker(
    llm=llm,
    openai_api_key=api_key,
    ai_name="Bumble Bee",
    ai_role="Worker in a swarm",
    external_tools = None,
    human_in_the_loop = False,
    temperature = 0.5,
)

node3 = Worker(
    llm=llm,
    openai_api_key=api_key,
    ai_name="Bumble Bee",
    ai_role="Worker in a swarm",
    external_tools = None,
    human_in_the_loop = False,
    temperature = 0.5,
)

agents = [
    node,
    node2,
    node3
]

# Initialize multi-agent debate with the selection function
debate = MultiAgentDebate(agents, select_speaker)

# Run task
task = "What were the winning boston marathon times for the past 5 years (ending in 2022)? Generate a table of the year, name, country of origin, and times."
results = debate.run(task, max_iters=4)

# Print results
for result in results:
    print(f"Agent {result['agent']} responded: {result['response']}")

---

Worker

• The Worker is an fully feature complete agent with an llm, tools, and a vectorstore for long term memory!
• Place your api key as parameters in the llm if you choose!
• And, then place the openai api key in the Worker for the openai embedding model

from swarms.models import OpenAIChat
from swarms import Worker

api_key = ""

llm = OpenAIChat(
    openai_api_key=api_key,
    temperature=0.5,
)

node = Worker(
    llm=llm,
    ai_name="Optimus Prime",
    openai_api_key=api_key,
    ai_role="Worker in a swarm",
    external_tools=None,
    human_in_the_loop=False,
    temperature=0.5,
)

task = "What were the winning boston marathon times for the past 5 years (ending in 2022)? Generate a table of the year, name, country of origin, and times."
response = node.run(task)
print(response)

---

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")

---

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.

Optimization Priorities

1. Reliability: Increase the reliability of the swarm - obtaining the desired output with a basic and un-detailed input.

2. Speed: Reduce the time it takes for the swarm to accomplish tasks by improving the communication layer, critiquing, and self-alignment with meta prompting.

3. Scalability: Ensure that the system is asynchronous, concurrent, and self-healing to support scalability.

Our goal is to continuously improve Swarms by following this roadmap, while also being adaptable to new needs and opportunities as they arise.

License

MIT