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Swarms

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

Vision

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!

`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!


from swarms.models import OpenAIChat
from swarms.structs import Agent

api_key = ""

# Initialize the language model, this model can be swapped out with Anthropic, ETC, Huggingface Models like Mistral, ETC
llm = OpenAIChat(
    # model_name="gpt-4"
    openai_api_key=api_key,
    temperature=0.5,
    # max_tokens=100,
)

## Initialize the workflow
agent = Agent(
    llm=llm,
    max_loops=2,
    dashboard=True,
    # stopping_condition=None,  # You can define a stopping condition as needed.
    # loop_interval=1,
    # retry_attempts=3,
    # retry_interval=1,
    # interactive=False,  # Set to 'True' for interactive mode.
    # dynamic_temperature=False,  # Set to 'True' for dynamic temperature handling.
)

# out = agent.load_state("flow_state.json")
# temp = agent.dynamic_temperature()
# filter = agent.add_response_filter("Trump")
out = agent.run("Generate a 10,000 word blog on health and wellness.")
# out = agent.validate_response(out)
# out = agent.analyze_feedback(out)
# out = agent.print_history_and_memory()
# # out = agent.save_state("flow_state.json")
# 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

from swarms.models import OpenAIChat
from swarms.structs import Agent
from swarms.structs.sequential_workflow import SequentialWorkflow

# Example usage
api_key = (
    ""  # Your actual API key here
)

# 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, dashboard=False)

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

agent3 = Agent(llm=llm, max_loops=1, dashboard=False)

# 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.

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 here on Discord!

License

MIT

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