![Swarming banner icon](images/swarmslogobanner.png)

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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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----

## Installation
`pip3 install --upgrade swarms`

---

## Usage

Run example in Collab: <a target="_blank" href="https://colab.research.google.com/github/kyegomez/swarms/blob/master/playground/swarms_example.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>

### `Agent` 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: `Agent` is designed and optimized for automating real-world tasks at scale!

```python
import os

from dotenv import load_dotenv

# Import the OpenAIChat model and the Agent struct
from swarms.models import OpenAIChat
from swarms.structs import Agent

# Load the environment variables
load_dotenv()

# Get the API key from the environment
api_key = os.environ.get("OPENAI_API_KEY")

# Initialize the language model
llm = OpenAIChat(
    temperature=0.5,
    openai_api_key=api_key,
)


## Initialize the workflow
agent = Agent(llm=llm, max_loops=1, autosave=True, dashboard=True)

# Run the workflow on a task
out = agent.run("Generate a 10,000 word blog on health and wellness.")
print(out)


```

------

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

```python
import os 
from swarms.models import OpenAIChat
from swarms.structs import Agent
from swarms.structs.sequential_workflow import SequentialWorkflow
from dotenv import load_dotenv

load_dotenv()

# Load the environment variables
api_key = os.getenv("OPENAI_API_KEY")


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


# Initialize the agent with the language agent
agent1 = Agent(llm=llm, max_loops=1)

# Create another agent for a different task
agent2 = Agent(llm=llm, max_loops=1)

# Create another agent for a different task
agent3 = Agent(llm=llm, max_loops=1)

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

# Add tasks to the workflow
workflow.add(
    agent1, "Generate a 10,000 word blog on health and wellness.", 
)

# Suppose the next task takes the output of the first task as input
workflow.add(
    agent2, "Summarize the generated blog",
)

# Run the workflow
workflow.run()

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


```

## `Multi Modal Autonomous Agents`
- Run the agent with multiple modalities useful for various real-world tasks in manufacturing, logistics, and health.

```python
# Description: This is an example of how to use the Agent class to run a multi-modal workflow
import os
from dotenv import load_dotenv
from swarms.models.gpt4_vision_api import GPT4VisionAPI
from swarms.structs import Agent

# Load the environment variables
load_dotenv()

# Get the API key from the environment
api_key = os.environ.get("OPENAI_API_KEY")

# Initialize the language model
llm = GPT4VisionAPI(
    openai_api_key=api_key,
    max_tokens=500,
)

# Initialize the task
task = (
    "Analyze this image of an assembly line and identify any issues such as"
    " misaligned parts, defects, or deviations from the standard assembly"
    " process. IF there is anything unsafe in the image, explain why it is"
    " unsafe and how it could be improved."
)
img = "assembly_line.jpg"

## Initialize the workflow
agent = Agent(
    llm=llm,
    max_loops="auto",
    autosave=True,
    dashboard=True,
    multi_modal=True
)

# Run the workflow on a task
out = agent.run(task=task, img=img)
print(out)


```

---

# Features 🤖 
The Swarms framework is designed with a strong emphasis on reliability, performance, and production-grade readiness. 
Below are the key features that make Swarms an ideal choice for enterprise-level AI deployments.

## 🚀 Production-Grade Readiness
- **Scalable Architecture**: Built to scale effortlessly with your growing business needs.
- **Enterprise-Level Security**: Incorporates top-notch security features to safeguard your data and operations.
- **Containerization and Microservices**: Easily deployable in containerized environments, supporting microservices architecture.

## ⚙️ Reliability and Robustness
- **Fault Tolerance**: Designed to handle failures gracefully, ensuring uninterrupted operations.
- **Consistent Performance**: Maintains high performance even under heavy loads or complex computational demands.
- **Automated Backup and Recovery**: Features automatic backup and recovery processes, reducing the risk of data loss.

## 💡 Advanced AI Capabilities

The Swarms framework is equipped with a suite of advanced AI capabilities designed to cater to a wide range of applications and scenarios, ensuring versatility and cutting-edge performance.

### Multi-Modal Autonomous Agents
- **Versatile Model Support**: Seamlessly works with various AI models, including NLP, computer vision, and more, for comprehensive multi-modal capabilities.
- **Context-Aware Processing**: Employs context-aware processing techniques to ensure relevant and accurate responses from agents.

### Function Calling Models for API Execution
- **Automated API Interactions**: Function calling models that can autonomously execute API calls, enabling seamless integration with external services and data sources.
- **Dynamic Response Handling**: Capable of processing and adapting to responses from APIs for real-time decision making.

### Varied Architectures of Swarms
- **Flexible Configuration**: Supports multiple swarm architectures, from centralized to decentralized, for diverse application needs.
- **Customizable Agent Roles**: Allows customization of agent roles and behaviors within the swarm to optimize performance and efficiency.

### Generative Models
- **Advanced Generative Capabilities**: Incorporates state-of-the-art generative models to create content, simulate scenarios, or predict outcomes.
- **Creative Problem Solving**: Utilizes generative AI for innovative problem-solving approaches and idea generation.

### Enhanced Decision-Making
- **AI-Powered Decision Algorithms**: Employs advanced algorithms for swift and effective decision-making in complex scenarios.
- **Risk Assessment and Management**: Capable of assessing risks and managing uncertain situations with AI-driven insights.

### Real-Time Adaptation and Learning
- **Continuous Learning**: Agents can continuously learn and adapt from new data, improving their performance and accuracy over time.
- **Environment Adaptability**: Designed to adapt to different operational environments, enhancing robustness and reliability.


## 🔄 Efficient Workflow Automation
- **Streamlined Task Management**: Simplifies complex tasks with automated workflows, reducing manual intervention.
- **Customizable Workflows**: Offers customizable workflow options to fit specific business needs and requirements.
- **Real-Time Analytics and Reporting**: Provides real-time insights into agent performance and system health.

## 🌐 Wide-Ranging Integration
- **API-First Design**: Easily integrates with existing systems and third-party applications via robust APIs.
- **Cloud Compatibility**: Fully compatible with major cloud platforms for flexible deployment options.
- **Continuous Integration/Continuous Deployment (CI/CD)**: Supports CI/CD practices for seamless updates and deployment.

## 📊 Performance Optimization
- **Resource Management**: Efficiently manages computational resources for optimal performance.
- **Load Balancing**: Automatically balances workloads to maintain system stability and responsiveness.
- **Performance Monitoring Tools**: Includes comprehensive monitoring tools for tracking and optimizing performance.

## 🛡️ Security and Compliance
- **Data Encryption**: Implements end-to-end encryption for data at rest and in transit.
- **Compliance Standards Adherence**: Adheres to major compliance standards ensuring legal and ethical usage.
- **Regular Security Updates**: Regular updates to address emerging security threats and vulnerabilities.

## 💬 Community and Support
- **Extensive Documentation**: Detailed documentation for easy implementation and troubleshooting.
- **Active Developer Community**: A vibrant community for sharing ideas, solutions, and best practices.
- **Professional Support**: Access to professional support for enterprise-level assistance and guidance.

Swarms framework is not just a tool but a robust, scalable, and secure partner in your AI journey, ready to tackle the challenges of modern AI applications in a business environment.


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


## 🫶 Contributions:

Swarms is an open-source project, and contributions are welcome. If you want to contribute, you can create new features, fix bugs, or improve the infrastructure. Please refer to the [CONTRIBUTING.md](https://github.com/kyegomez/swarms/blob/master/CONTRIBUTING.md) and our [contributing board](https://github.com/users/kyegomez/projects/1) file in the repository for more information on how to contribute.

To see how to contribute, visit [Contribution guidelines](https://github.com/kyegomez/swarms/blob/master/CONTRIBUTING.md)

<a href="https://github.com/kyegomez/swarms/graphs/contributors">
  <img src="https://contrib.rocks/image?repo=kyegomez/swarms" />
</a>


## Community
- [Join the Swarms community on Discord!](https://discord.gg/AJazBmhKnr)
- Join our Swarms Community Gathering every Thursday at 1pm NYC Time to unlock the potential of autonomous agents in automating your daily tasks [Sign up here](https://lu.ma/5p2jnc2v)



## Discovery Call
Book a discovery call with the Swarms team to learn how to optimize and scale your swarm! [Click here to book a time that works for you!](https://calendly.com/swarm-corp/30min?month=2023-11)

# License
MIT
