# 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](https://calendly.com/swarm-corp/30min), 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!](https://docs.apac.ai) ### `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 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 ```python 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. ### 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) ## 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](CONTRIBUTING.md) and our [contributing board](https://github.com/users/kyegomez/projects/1) ## Community - [Join the Swarms community here on Discord!](https://discord.gg/AJazBmhKnr) # License MIT