swarms/README.md

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


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## 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](https://calendly.com/apacai/agora), the Creator, to discuss any issues, provide feedback, or explore how we can improve Swarms for you.


----------

## Installation
* `pip3 install 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!](https://docs.apac.ai)

### `MultiAgentDebate`

- `MultiAgentDebate` is a simple class that enables multi agent collaboration.

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

llm = OpenAIChat(
    model_name='gpt-4', 
    openai_api_key="api-key", 
    temperature=0.5
)

node = Worker(
    llm=llm,
    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,
    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,
    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

```python
from swarms.models import ChatOpenAI
from swarms.workers import Worker

llm = ChatOpenAI(
    model_name='gpt-4', 
    openai_api_key="api-key", 
    temperature=0.5
)

node = Worker(
    llm=llm,
    ai_name="Optimus Prime",
    #openai key for the embeddings
    openai_api_key="sk-eee"
    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:

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


llm = OpenAIChat(model_name="gpt-4")

agent = OmniModalAgent(llm)

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

```

- OmniModal Agent has a ui in the root called `python3 omni_ui.py`

---

# Documentation
For documentation, go here, [swarms.apac.ai](https://swarms.apac.ai)

**NOTE: We need help building the documentation**

-----

# ✨ Features
* Easy to use Base LLMs, `OpenAI` `Palm` `Anthropic` `HuggingFace`
* Enterprise Grade, Production Ready with robust Error Handling
* Multi-Modality Native with Multi-Modal LLMs as tools
* Infinite Memory Processing: Store infinite sequences of infinite Multi-Modal data, text, images, videos, audio
* Usability: Extreme emphasis on useability, code is at it's theortical minimum simplicity factor to use
* Reliability: Outputs that accomplish tasks and activities you wish to execute.
* Fluidity: A seamless all-around experience to build production grade workflows
* Speed: Lower the time to automate tasks by 90%. 
* Simplicity: Swarms is extremely simple to use, if not thee simplest agent framework of all time
* Powerful: Swarms is capable of building entire software apps, to large scale data analysis, and handling chaotic situations


-----

## 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](C0NTRIBUTING.md).

---
## 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.

---

## The Plan

### Phase 1: Building the Foundation
In the first phase, our focus is on building the basic infrastructure of Swarms. This includes developing key components like the Swarms class, integrating essential tools, and establishing task completion and evaluation logic. We'll also start developing our testing and evaluation framework during this phase. If you're interested in foundational work and have a knack for building robust, scalable systems, this phase is for you.

### Phase 2: Optimizing the System
In the second phase, we'll focus on optimizng Swarms by integrating more advanced features, improving the system's efficiency, and refining our testing and evaluation framework. This phase involves more complex tasks, so if you enjoy tackling challenging problems and contributing to the development of innovative features, this is the phase for you.

### Phase 3: Towards Super-Intelligence
The third phase of our bounty program is the most exciting - this is where we aim to achieve super-intelligence. In this phase, we'll be working on improving the swarm's capabilities, expanding its skills, and fine-tuning the system based on real-world testing and feedback. If you're excited about the future of AI and want to contribute to a project that could potentially transform the digital world, this is the phase for you.

Remember, our roadmap is a guide, and we encourage you to bring your own ideas and creativity to the table. We believe that every contribution, no matter how small, can make a difference. So join us on this exciting journey and help us create the future of Swarms.

<!-- **To participate in our bounty program, visit the [Swarms Bounty Program Page](https://swarms.ai/bounty).** Let's build the future together! -->


# Demos

![Swarms Demo](images/Screenshot_48.png)

## Swarm Video Demo {Click for more}

[![Watch the swarm video](https://img.youtube.com/vi/Br62cDMYXgc/maxresdefault.jpg)](https://youtu.be/Br62cDMYXgc)

---

# Contact 
For enterprise and production ready deployments, allow us to discover more about you and your story, [book a call with us here](https://www.apac.ai/Setup-Call)