![\"GitHub](\"https://img.shields.io/badge/GitHub-Repo-green.svg\"){kind=icon}
\n ![\"npm\"](\"https://img.shields.io/npm/v/agent-squad.svg?style=flat-square\")
\n ![\"PyPI\"](\"https://img.shields.io/pypi/v/agent-squad.svg?style=flat-square\")
\nFlexible, lightweight open-source framework for orchestrating multiple AI agents to handle complex conversations.
\n\n---\n\n 📢 New Name Alert: Multi-Agent Orchestrator is now Agent Squad! 🎉
\n Same powerful functionalities, new catchy name. Embrace the squad!\n
\n \n \n \n
\n \n ![\"GitHub](\"https://img.shields.io/github/stars/awslabs/agent-squad?style=social\")
![\"GitHub](\"https://img.shields.io/github/forks/awslabs/agent-squad?style=social\")
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\n \n ![\"Last](\"https://img.shields.io/github/last-commit/awslabs/agent-squad\")
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\n \n 📚 Explore Full Documentation\n \n
\n\n\n## 🔖 Features\n\n- 🧠 **Intelligent intent classification** — Dynamically route queries to the most suitable agent based on context and content.\n- 🔤 **Dual language support** — Fully implemented in both **Python** and **TypeScript**.\n- 🌊 **Flexible agent responses** — Support for both streaming and non-streaming responses from different agents.\n- 📚 **Context management** — Maintain and utilize conversation context across multiple agents for coherent interactions.\n- 🔧 **Extensible architecture** — Easily integrate new agents or customize existing ones to fit your specific needs.\n- 🌐 **Universal deployment** — Run anywhere - from AWS Lambda to your local environment or any cloud platform.\n- 📦 **Pre-built agents and classifiers** — A variety of ready-to-use agents and multiple classifier implementations available.\n\n\n## What's the Agent Squad ❓\n\nThe Agent Squad is a flexible framework for managing multiple AI agents and handling complex conversations. It intelligently routes queries and maintains context across interactions.\n\nThe system offers pre-built components for quick deployment, while also allowing easy integration of custom agents and conversation messages storage solutions.\n\nThis adaptability makes it suitable for a wide range of applications, from simple chatbots to sophisticated AI systems, accommodating diverse requirements and scaling efficiently.\n\n\n
\n ![](\"https://astro.badg.es/v2/built-with-starlight/tiny.svg\")
\n
![](\"/agent-squad/chat-demo-app.png\")
\n\nIn the screen recording below, we demonstrate an extended version of the demo app that uses 6 specialized agents:\n- **Travel Agent**: Powered by an Amazon Lex Bot\n- **Weather Agent**: Utilizes a Bedrock LLM Agent with a tool to query the open-meteo API\n- **Math Agent**: Utilizes a Bedrock LLM Agent with two tools for executing mathematical operations\n- **Tech Agent**: A Bedrock LLM Agent designed to offer technical support and documentation assistance with direct access to **Agent Squad framework source code**\n- **Health Agent**: A Bedrock LLM Agent focused on addressing health-related queries\n\nWatch as the system seamlessly switches context between diverse topics, from booking flights to checking weather, solving math problems, and providing health information.\nNotice how the appropriate agent is selected for each query, maintaining coherence even with brief follow-up inputs.\n\nThe demo highlights the system's ability to handle complex, multi-turn conversations while preserving context and leveraging specialized agents across various domains.\n\n![](\"/agent-squad/demo-app.gif\")
\n\n\n## 📋 Prerequisites\n\nBefore deploying the demo web app, ensure you have the following:\n\n1. An AWS account with appropriate permissions\n2. AWS CLI installed and configured with your credentials\n3. Node.js and npm installed on your local machine\n4. AWS CDK CLI installed (`npm install -g aws-cdk`)\n\n## 🚀 Deployment Steps\n\nFollow these steps to deploy the demo chat web application:\n\n1. **Clone the Repository** (if you haven't already):\n ```\n git clone https://github.com/awslabs/agent-squad.git\n cd agent-squad\n ```\n\n2. **Navigate to the Demo Web App Directory**:\n ```\n cd examples/chat-demo-app\n ```\n\n3. **Install Dependencies**:\n ```\n npm install\n ```\n\n4. **Bootstrap AWS CDK** (if you haven't used CDK in this AWS account/region before):\n\n ```\n cdk bootstrap aws://123456789012/us-east-1\n ```\n replace `123456789012` with your account id.\n This chat-demo application is using the default [BedrockClassifier](http://localhost:4321/agent-squad/classifiers/built-in/bedrock-classifier#basic-usage) with Claude 3.5 Sonnet v1. Make sure to use a region where this model is available. If you plan on using a region different from us-east-1 (e.g us-west-2), make sure to also bootstrap us-east-1 region as well as the CDK stack also deploys infra in this region (lambda@edge function).\n\n5. **Review and Customize the Stack** (optional):\n Open `chat-demo-app/cdk.json` and review the configuration. You can customize aspects of the deployment by enabling or disabling additional agents.\n\n ```\n \"context\": {\n \"enableLexAgent\": true,\n ...\n ```\n\n **enableLexAgent:** Enable the sample Airlines Bot (See AWS Blogpost [here](https://aws.amazon.com/blogs/machine-learning/automate-the-customer-service-experience-for-flight-reservations-using-amazon-lex/))\n\n\n6. **Deploy the Application**:\n ```\n export AWS_DEFAULT_REGION=us-east-1\n cdk deploy --all\n ```\n\n7. **Confirm Deployment**:\n CDK will show you a summary of the changes and ask for confirmation. Type 'y' and press Enter to proceed.\n\n8. **Note the Outputs**:\n After deployment, CDK will display outputs including the URL of your deployed web app and the user pool ID.\n Save the URL and the user pool Id.\n\n9. **Create a user in Amazon Cognito user pool**:\n```\naws cognito-idp admin-create-user \\\n --user-pool-id your-region_xxxxxxx \\\n --username your@email.com \\\n --user-attributes Name=email,Value=your@email.com \\\n --temporary-password \"MyChallengingPassword\" \\\n --message-action SUPPRESS \\\n --region your-region\n````\n\n## 🌐 Accessing the Demo Web App\n\nOnce deployment is complete, you can access the demo chat web application by:\n\n1. Opening the URL provided in the CDK outputs in your web browser.\n2. Logging in with the temporary credentials provided (if applicable).\n\n\n## ✅ Testing the Deployment\n\nTo ensure the deployment was successful:\n\n1. Open the web app URL in your browser.\n2. Try sending a few messages to test the multi-agent system.\n3. Verify that you receive appropriate responses from different agents.\n\n## 🧹 Cleaning Up\n\nTo avoid incurring unnecessary AWS charges, remember to tear down the deployment when you're done:\n\n```\ncdk destroy\n```\n\nConfirm the deletion when prompted.\n\n## 🛠️ Troubleshooting\n\nIf you encounter issues during deployment:\n\n1. Ensure your AWS credentials are correctly configured.\n2. Check that you have the necessary permissions in your AWS account.\n3. Verify that all dependencies are correctly installed.\n4. Review the AWS CloudFormation console for detailed error messages if the deployment fails.\n\n## ➡️ Next Steps\n\nAfter successfully deploying the demo web app, you can:\n\n1. Customize the web interface in the source code.\n2. Modify the agent configurations to test different scenarios.\n3. Integrate additional AWS services to enhance the application's capabilities.\n\nBy deploying this demo web app, you can interact with your Agent Squad System in a user-friendly interface, showcasing its capabilities and helping you understand how it performs in a real-world scenario.\n\n## ⚠️ Disclamer\nThis demo application is intended solely for demonstration purposes. It is not designed for handling, storing, or processing any kind of Personally Identifiable Information (PII) or personal data. Users are strongly advised not to enter, upload, or use any PII or personal data within this application. Any use of PII or personal data is at the user's own risk and the developers of this application shall not be held responsible for any data breaches, misuse, or any other related issues. Please ensure that all data used in this demo is non-sensitive and anonymized.\n\nFor production usage, it is crucial to implement proper security measures to protect PII and personal data. This includes obtaining proper permissions from users, utilizing encryption for data both in transit and at rest, and adhering to industry standards and regulations to maximize security. Failure to do so may result in data breaches and other serious security issues.\n\nReady to build your own multi-agent chat application? Check out the complete [source code](https://github.com/awslabs/agent-squad/tree/main/examples/chat-demo-app) in our GitHub repository.\n"
},
{
"path": "docs/src/content/docs/cookbook/examples/ecommerce-support-simulator.md",
"content": "---\ntitle: AI-Powered E-commerce Support Simulator\ndescription: How to deploy the demo AI-Powered E-commerce Support Simulator\n---\n\nThis project demonstrates the practical application of AI agents and human-in-the-loop interactions in an e-commerce support context. It showcases how AI can handle customer queries efficiently while seamlessly integrating human support when needed.\n\n## Overview\n\nThe AI-Powered E-commerce Support Simulator is designed to showcase a sophisticated customer support system that combines AI agents with human support. It demonstrates how AI can handle routine queries automatically while routing complex issues to human agents, providing a comprehensive support experience.\n\n## Features\n\n- AI-powered response generation for common queries\n- Intelligent routing of complex issues to human support\n- Real-time chat functionality\n- Email-style communication option\n\n## UI Modes\n\n### Chat Mode\n\nThe Chat Mode provides a real-time conversation interface, simulating instant messaging between customers and the support system. It features:\n\n- Separate chat windows for customer and support perspectives\n- Real-time message updates\n- Automatic scrolling to the latest message\n\n\n\n### Email Mode\n\nThe Email Mode simulates asynchronous email communication. It includes:\n\n- Email composition interfaces for both customer and support\n- Pre-defined email templates for common scenarios\n- Response viewing areas for both parties\n\n\n\n## Mock Data\n\nThe project includes a `mock_data.json` file for testing and demonstration purposes. This file contains sample data that simulates various customer scenarios, product information, and order details.\n\nTo view and use the mock data:\n\n1. Navigate to the `public` directory in the project.\n2. Open the `mock_data.json` file to view its contents.\n3. Use the provided data to test different support scenarios and observe how the system handles various queries.\n\n## AI and Human Interaction\n\nThis simulator demonstrates the seamless integration of AI agents and human support:\n\n- Automated Handling: AI agents automatically process and respond to common or straightforward queries.\n- Human Routing: Complex or sensitive issues are identified and routed to human support agents.\n- Customer Notification: When a query is routed to human support, the customer receives an automatic confirmation.\n- Support Interface: The support side of the interface allows human agents to see which messages require their attention and respond accordingly.\n- Handoff Visibility: Users can observe when a query is handled by AI and when it's transferred to a human agent.\n\nThis simulator serves as a practical example of how AI and human support can be integrated effectively in a customer service environment. It demonstrates the potential for enhancing efficiency while maintaining the ability to provide personalized, human touch when necessary.# AI-Powered E-commerce Support Simulator\n\nA demonstration of how AI agents and human support can work together in an e-commerce customer service environment. This project showcases intelligent query routing, multi-agent collaboration, and seamless human integration for complex support scenarios.\n\n## 🎯 Key Features\n\n- Multi-agent AI orchestration\n- Real-time and asynchronous communication modes\n- Integration with human support workflow\n- Tool-augmented AI interactions\n- Production-ready AWS architecture\n- Mock data for realistic scenarios\n\n## 🏗️ Architecture\n\n### Agent Architecture\n\n\n\nThe system employs three specialized agents:\n\n#### 1. Order Management Agent (Claude 3 Sonnet)\n- 🎯 **Purpose**: Handles order-related inquiries\n- 🛠️ **Tools**:\n - `orderlookup`: Retrieves order details\n - `shipmenttracker`: Tracks shipping status\n - `returnprocessor`: Manages returns\n- ✨ **Capabilities**:\n - Real-time order tracking\n - Return processing\n - Refund handling\n\n#### 2. Product Information Agent (Claude 3 Haiku)\n- 🎯 **Purpose**: Product information and specifications\n- 🧠 **Knowledge Base**: Integrated product database\n- ✨ **Capabilities**:\n - Product specifications\n - Compatibility checking\n - Availability information\n\n#### 3. Human Agent\n- 🎯 **Purpose**: Complex case handling and oversight\n- ✨ **Capabilities**:\n - Complex complaint resolution\n - Critical decision oversight\n - AI response verification\n\n### AWS Infrastructure\n\n\n\n#### Core Components\n- 🌐 **Frontend**: React + CloudFront\n- 🔌 **API**: AppSync GraphQL\n- 📨 **Messaging**: SQS queues\n- ⚡ **Processing**: Lambda functions\n- 💾 **Storage**: DynamoDB + S3\n- 🔐 **Auth**: Cognito\n\n## 💬 Communication Modes\n\n### Real-Time Chat\n\n- Instant messaging interface\n- Real-time response streaming\n- Automatic routing\n\n### Email-Style\n\n- Asynchronous communication\n- Template-based responses\n- Structured conversations\n\n## 🛠️ Mock System Integration\n\n### Mock Data Structure\nThe `mock_data.json` provides realistic test data:\n```json\n{\n \"orders\": {...},\n \"products\": {...},\n \"shipping\": {...}\n}\n```\n\n### Tool Integration\n- Order management tools use mock database\n- Shipment tracking simulates real-time updates\n- Return processing demonstrates workflow\n\n## 🚀 Deployment Guide\n\n### Prerequisites\n- AWS account with permissions\n- AWS CLI configured\n- Node.js and npm\n- AWS CDK CLI\n\n### Quick Start\n```bash\n# Clone repository\ngit clone https://github.com/awslabs/agent-squad.git\ncd agent-squad/examples/ecommerce-support-simulator\n\n# Install and deploy\nnpm install\ncdk bootstrap\ncdk deploy\n\n# Create user\naws cognito-idp admin-create-user \\\n --user-pool-id your-region_xxxxxxx \\\n --username your@email.com \\\n --user-attributes Name=email,Value=your@email.com \\\n --temporary-password \"MyChallengingPassword\" \\\n --message-action SUPPRESS \\\n --region your-region\n```\n\n## 🔍 Demo Scenarios\n\n1. **Order Management**\n - Order status inquiries\n - Shipment tracking\n - Return requests\n\n2. **Product Support**\n - Product specifications\n - Compatibility checks\n - Availability queries\n\n3. **Complex Cases**\n - Multi-step resolutions\n - Human escalation\n - Critical decisions\n\n## 🧹 Cleanup\n```bash\ncdk destroy\n```\n\n## 🔧 Troubleshooting\n\nCommon issues and solutions:\n1. **Deployment Failures**\n - Verify AWS credentials\n - Check permissions\n - Review CloudFormation logs\n\n2. **Runtime Issues**\n - Validate mock data format\n - Check queue configurations\n - Verify Lambda logs\n\n## ⚠️ Disclaimer\n\nThis demo application is intended solely for demonstration purposes. It is not designed for handling, storing, or processing any kind of Personally Identifiable Information (PII) or personal data. Users are strongly advised not to enter, upload, or use any PII or personal data within this application. Any use of PII or personal data is at the user's own risk and the developers of this application shall not be held responsible for any data breaches, misuse, or any other related issues. Please ensure that all data used in this demo is non-sensitive and anonymized.\n\nFor production usage, it is crucial to implement proper security measures to protect PII and personal data. This includes obtaining proper permissions from users, utilizing encryption for data both in transit and at rest, and adhering to industry standards and regulations to maximize security. Failure to do so may result in data breaches and other serious security issues.\n## 📚 Additional Resources\n\n- [Agent Squad Documentation](https://github.com/awslabs/agent-squad)\n- [AWS AppSync Documentation](https://docs.aws.amazon.com/appsync)\n- [Claude API Documentation](https://docs.anthropic.com/en/api/getting-started)\n\n\nReady to build your own multi-agent chat application? Check out the complete [source code](https://github.com/awslabs/agent-squad/tree/main/examples/ecommerce-support-simulator) in our GitHub repository.\n"
},
{
"path": "docs/src/content/docs/cookbook/examples/fast-api-streaming.md",
"content": "---\ntitle: FastAPI Streaming\ndescription: How to deploy use FastAPI Streaming with Agent Squad\n---\n\nThis example demonstrates how to implement streaming responses with the Agent Squad using FastAPI. It shows how to build a simple API that streams responses from multiple AI agents in real-time.\n\n## Features\n- Real-time streaming responses using FastAPI's `StreamingResponse`\n- Custom streaming handler implementation\n- Multiple agent support (Tech and Health agents)\n- Queue-based token streaming\n- CORS-enabled API endpoint\n\n## Quick Start\n```bash\n# Install dependencies\npip install \"fastapi[all]\" agent-squad\n\n# Run the server\nuvicorn app:app --reload\n```\n\n## API Endpoint\n\n```bash\nPOST /stream_chat/\n```\n\nRequest body:\n```json\n{\n \"content\": \"your question here\",\n \"user_id\": \"user123\",\n \"session_id\": \"session456\"\n}\n```\n\n## Implementation Highlights\n- Uses FastAPI's event streaming capabilities\n- Custom callback handler for real-time token streaming\n- Thread-safe queue implementation for token management\n- Configurable orchestrator with multiple specialized agents\n- Error handling and proper stream closure\n\n## Example Usage\n```python\nimport requests\n\nresponse = requests.post(\n 'http://localhost:8000/stream_chat/',\n json={\n 'content': 'What are the latest AI trends?',\n 'user_id': 'user123',\n 'session_id': 'session456'\n },\n stream=True\n)\n\nfor chunk in response.iter_content():\n print(chunk.decode(), end='', flush=True)\n```\n\nReady to build your own multi-agent chat application? Check out the complete [source code](https://github.com/awslabs/agent-squad/tree/main/examples/fast-api-streaming) in our GitHub repository.\n"
},
{
"path": "docs/src/content/docs/cookbook/examples/ollama-agent.mdx",
"content": "---\ntitle: Ollama Agent\ndescription: A guide to creating an Ollama agent and integrating it into the Agent Squad System.\n---\n\nWelcome to the Ollama Agent guide! This example will walk you through creating an Ollama agent and integrating it into your Agent Squad System.\nLet's dive in!\n\n## 📚Prerequisites:\n- Basic knowledge of TypeScript or Python\n- Familiarity with the Agent Squad System\n- [Ollama installed](https://ollama.com/download) on your machine\n\n## 💾 1. Ollama installation:\n\nimport { Tabs, TabItem } from '@astrojs/starlight/components';\n\n\n![](\"/agent-squad/flow.jpg\")
\n
\n {children}\n \n \n ) : (\n \n {children}\n \n );\n },\n p: ({ node, ...props }) => ,\n a: ({ node, ...props }) => ,\n h1: ({ node, ...props }) => ,\n h2: ({ node, ...props }) => ,\n h3: ({ node, ...props }) => ,\n ul: ({ node, ...props }) => \n Experience the power of intelligent routing and context management\n across multiple AI agents.\n
\n\n Type \"hello\" or \"bonjour\" to see the available agents, or ask questions like \"How do I use agents?\", \"How can I use the framework to create a custom agent?\", \"What are the steps to customize an agent?\"\n
\n\n {msg.sender}\n
\n {msg.isWaiting ? (\n\n {getRandomWaitMessage()}\n
\n\n {new Date(msg.timestamp).toLocaleTimeString()}\n
\nTo learn more about the Agent Squad:
\n{text}
\n\n {senderLabel}\n
\n\n {new Date(msg.timestamp).toLocaleTimeString()}\n
\n{customerResponse}
\n{supportResponse}
\n\n Experience the future of customer support with our multi-agent AI system.\n
\n\n Try asking \"Where is my order?\" or \"I want to return an item\" to see how our AI handles customer support inquiries!\n
\n\n Customer Interface\n
\n\n Customer's view of the interaction\n
\n\n Support Center Interface\n
\n\n Human agent's view and responses\n
\nTo learn more about the Agent Squad:
\nFlexible and powerful framework for managing multiple AI agents and handling complex conversations.
\n\n\n\n \n \n ![\"Documentation\"](\"https://img.shields.io/badge/docs-book-blue.svg?style=flat-square\"){kind=icon}
\n
\n \n
\n \n
\n \n ![\"PyPI](\"https://img.shields.io/pypi/dm/agent-squad?label=pypi%20downloads\")
![\"Python](\"https://img.shields.io/static/v1?label=python&message=%203.11|%203.12|%203.13&color=blue?style=flat-square&logo=python\")
![\"Demo](\"https://raw.githubusercontent.com/awslabs/agent-squad/main/img/demo-app.jpg\")
\n\n\n\n\n## 🚀 Getting Started\n\nCheck out our [documentation](https://awslabs.github.io/agent-squad/) for comprehensive guides on setting up and using the Agent Squad!\n\n\n\n### Core Installation\n\n```bash\n# Optional: Set up a virtual environment\npython -m venv venv\nsource venv/bin/activate # On Windows use `venv\\Scripts\\activate`\npip install agent-squad[aws]\n```\n\n#### Default Usage\n\nHere's an equivalent Python example demonstrating the use of the Agent Squad with a Bedrock LLM Agent and a Lex Bot Agent:\n\n```python\nimport sys\nimport asyncio\nfrom agent_squad.orchestrator import AgentSquad\nfrom agent_squad.agents import BedrockLLMAgent, LexBotAgent, BedrockLLMAgentOptions, LexBotAgentOptions, AgentStreamResponse\n\norchestrator = AgentSquad()\n\ntech_agent = BedrockLLMAgent(BedrockLLMAgentOptions(\n name=\"Tech Agent\",\n streaming=True,\n description=\"Specializes in technology areas including software development, hardware, AI, \\\n cybersecurity, blockchain, cloud computing, emerging tech innovations, and pricing/costs \\\n related to technology products and services.\",\n model_id=\"anthropic.claude-3-sonnet-20240229-v1:0\",\n))\norchestrator.add_agent(tech_agent)\n\n\nhealth_agent = BedrockLLMAgent(BedrockLLMAgentOptions(\n name=\"Health Agent\",\n streaming=True,\n description=\"Specializes in health and well being\",\n))\norchestrator.add_agent(health_agent)\n\nasync def main():\n # Example usage\n response = await orchestrator.route_request(\n \"What is AWS Lambda?\",\n 'user123',\n 'session456',\n {},\n True\n )\n\n # Handle the response (streaming or non-streaming)\n if response.streaming:\n print(\"\\n** RESPONSE STREAMING ** \\n\")\n # Send metadata immediately\n print(f\"> Agent ID: {response.metadata.agent_id}\")\n print(f\"> Agent Name: {response.metadata.agent_name}\")\n print(f\"> User Input: {response.metadata.user_input}\")\n print(f\"> User ID: {response.metadata.user_id}\")\n print(f\"> Session ID: {response.metadata.session_id}\")\n print(f\"> Additional Parameters: {response.metadata.additional_params}\")\n print(\"\\n> Response: \")\n\n # Stream the content\n async for chunk in response.output:\n async for chunk in response.output:\n if isinstance(chunk, AgentStreamResponse):\n print(chunk.text, end='', flush=True)\n else:\n print(f\"Received unexpected chunk type: {type(chunk)}\", file=sys.stderr)\n\n else:\n # Handle non-streaming response (AgentProcessingResult)\n print(\"\\n** RESPONSE ** \\n\")\n print(f\"> Agent ID: {response.metadata.agent_id}\")\n print(f\"> Agent Name: {response.metadata.agent_name}\")\n print(f\"> User Input: {response.metadata.user_input}\")\n print(f\"> User ID: {response.metadata.user_id}\")\n print(f\"> Session ID: {response.metadata.session_id}\")\n print(f\"> Additional Parameters: {response.metadata.additional_params}\")\n print(f\"\\n> Response: {response.output.content}\")\n\nif __name__ == \"__main__\":\n asyncio.run(main())\n```\n\nThe following example demonstrates how to use the Agent Squad with two different types of agents: a Bedrock LLM Agent with Converse API support and a Lex Bot Agent. This showcases the flexibility of the system in integrating various AI services.\n\n```python\n\n```\n\nThis example showcases:\n1. The use of a Bedrock LLM Agent with Converse API support, allowing for multi-turn conversations.\n2. Integration of a Lex Bot Agent for specialized tasks (in this case, travel-related queries).\n3. The orchestrator's ability to route requests to the most appropriate agent based on the input.\n4. Handling of both streaming and non-streaming responses from different types of agents.\n\n\n### Working with Anthropic or OpenAI\nIf you want to use Anthropic or OpenAI for classifier and/or agents, make sure to install the agent-squad with the relevant extra feature.\n```bash\npip install \"agent-squad[anthropic]\"\npip install \"agent-squad[openai]\"\n```\n\n### Full package installation\nFor a complete installation (including Anthropic and OpenAi):\n```bash\npip install agent-squad[all]\n```\n\n## Building Locally\n\nThis guide explains how to build and install the agent-squad package from source code.\n\n### Prerequisites\n\n- Python 3.11\n- pip package manager\n- Git (to clone the repository)\n\n### Building the Package\n\n1. Navigate to the Python package directory:\n ```bash\n cd python\n ```\n\n2. Install the build dependencies:\n ```bash\n python -m pip install build\n ```\n\n3. Build the package:\n ```bash\n python -m build\n ```\n\nThis process will create distribution files in the `python/dist` directory, including a wheel (`.whl`) file.\n\n### Installation\n\n1. Locate the current version number in `setup.cfg`.\n\n2. Install the built package using pip:\n ```bash\n pip install ./dist/agent_squad-Flexible and powerful framework for managing multiple AI agents and handling complex conversations.
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\n \n
\n \n
\n \n ![\"npm](\"https://img.shields.io/npm/dm/agent-squad?label=npm%20downloads\")