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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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-
-### Share on Social Media
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-
-## 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)
-
-### Example in Colab:
-
-
-
- Run example in Colab, using your OpenAI API key.
-
-### `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!
-
-```python
-
-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(
- # model_name="gpt-4"
- openai_api_key=api_key,
- temperature=0.5,
- # max_tokens=100,
-)
-
-## Initialize the workflow
-flow = Flow(
- 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 = flow.load_state("flow_state.json")
-# temp = flow.dynamic_temperature()
-# filter = flow.add_response_filter("Trump")
-out = flow.run("Generate a 10,000 word blog on health and wellness.")
-# out = flow.validate_response(out)
-# out = flow.analyze_feedback(out)
-# out = flow.print_history_and_memory()
-# # out = flow.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 Flow's with various LLMs and Multi-Modality Models
-
-```python
-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](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)
-
-# 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
+title: Swarms
+emoji: 💻
+colorFrom: indigo
+colorTo: blue
+sdk: gradio
+sdk_version: 4.8.0
+app_file: app.py
+pinned: false