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Swarming Language Models (Swarms)

Welcome to Swarms - the future of AI, where we leverage the power of autonomous agents to create 'swarms' of Language Models (LLM) that work together, creating a dynamic and interactive AI system.

Vision

In the world of AI and machine learning, individual models have made significant strides in understanding and generating human-like text. But imagine the possibilities when these models are no longer solitary units, but part of a cooperative and communicative swarm. This is the future we envision.

Just as a swarm of bees works together, communicating and coordinating their actions for the betterment of the hive, swarming LLM agents can work together to create richer, more nuanced outputs. By harnessing the strengths of individual agents and combining them through a swarming architecture, we can unlock a new level of performance and responsiveness in AI systems. We envision swarms of LLM agents revolutionizing fields like customer support, content creation, research, and much more.

Table of Contents

1. Installation
2. Usage
3. Sharing

Installation

git clone https://github.com/kyegomez/swarms.git
cd swarms
pip install -r requirements.txt

Usage

There are 2 methods, one is very simple to test it out and then there is another to explore the agents and so on! Check out the Documetation file to understand the classes

Method 1

Simple example python3 example.py

Method2

I see, my apologies for the confusion. Here is an updated usage example that just utilizes the BossNode class:

One of the main components of swarms is the BossNode class. This class acts as a "boss" that assigns tasks to other components.

Below is an example of how to use the BossNode class in Langchain:

from swarms import BossNode
#or swarms.agents.swarms import BossNode

# Initialize BossNode
boss_node = BossNode()

# Create and execute a task
task = boss_node.create_task("Write a summary of the latest news about artificial intelligence.")
boss_node.execute_task(task)

This will create a task for the BossNode, which is to write a summary of the latest news about artificial intelligence.

BossNode

The BossNode class is a key component of Swarms. It represents a "boss" in the system that assigns tasks to other components.

Here is an example of how you can use it:

class BossNode:
    def __init__(self, tools):
        # initialization code goes here

    def create_task(self, objective):
        return {"objective": objective}

    def execute_task(self, task):
        # task execution code goes here

With the BossNode class, you can create tasks for your tools to perform. For example, you can create a task to write a summary of a specific topic:

boss_node = BossNode()
task = boss_node.create_task("Write a summary of the latest news about quantum computing.")
boss_node.execute_task(task)

This will create and execute a task to write a summary about the latest news on quantum computing. The result will be the summary of the news.

Note

• The AutoAgent makes use of several helper tools and context managers for tasks such as processing CSV files, browsing web pages, and querying web pages. For the best use of this agent, understanding these tools is crucial.

• Additionally, the agent uses the ChatOpenAI, a language learning model (LLM), to perform its tasks. You need to provide an OpenAI API key to make use of it.

• Detailed knowledge of FAISS, a library for efficient similarity search and clustering of dense vectors, is also essential as it's used for memory storage and retrieval.

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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.

Thank you for being a part of our project!

To do:

• Integrate Multi Agent debate

• Integrate Multi agent2 debate

• Integrate meta prompting into all worker agents

• Create 1 main swarms class swarms('Increase sales by 40$', workers=4)

• Integrate Jarvis as worker nodes

• Integrate guidance and token healing

• Add text to speech whisper x, youtube script and text to speech code models as tools

• Add task completion logic with meta prompting, task evaluation as a state from 0.0 to 1.0, and critiquing for meta prompting.

• Integrate meta prompting for every agent boss and worker

• Get baby agi set up with the autogpt instance as a tool

• Integrate Ocean vector db as the main embedding database for all the agents boss and or worker

• Communication, a universal vector database that is only used when a task is completed in this format [TASK][COMPLETED]

• Create unit tests

• Create benchmrks

• Create evaluations

• Add new tool that uses WhiseperX to transcribe a youtube video

• Integrate hf agents as tools

• Integrate tools from here

• Create extensive and useful examples

• And, recreate exampels and usage in readme.