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

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Usage

Run example in Collab:

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(
    openai_api_key=api_key,
    temperature=0.5,
)

## Initialize the workflow
flow = Flow(
    llm=llm,
    max_loops=2,
    dashboard=True,

)

out = flow.run("Generate a 10,000 word blog on health and wellness.")

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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, BioGPT, Anthropic
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,
)

biochat = BioGPT()

# Use Anthropic
anthropic = Anthropic()

# Initialize the agent with the language flow
agent1 = Flow(llm=llm, max_loops=1, dashboard=False)

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

# Create another agent for a different task
agent3 = Flow(llm=biochat, max_loops=1, dashboard=False)

# agent4 = Flow(llm=anthropic, max_loops="auto")

# 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.", agent1)

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

workflow.add("Create a references sheet of materials for the curriculm", agent3)

# Run the workflow
workflow.run()

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

---

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

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 on Discord!
• 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

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!

License

MIT