{"id":31841544,"url":"https://github.com/vishapp/crewaimaster","last_synced_at":"2025-10-12T05:20:27.090Z","repository":{"id":307295574,"uuid":"1027224411","full_name":"VishApp/crewaimaster","owner":"VishApp","description":"CrewAIMaster transforms any task description into a fully functional, production-ready multi-agent system, making advanced AI orchestration accessible to everyone. 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It provides a CLI interface and comprehensive backend system for creating intelligent AI agents with memory, tools, and safety guardrails.\n\n## 📦 Installation\n\n```bash\n# install from PyPI (when available)\npip install crewaimaster\n\n# Or Install from source (recommended for development)\ngit clone https://github.com/VishApp/crewaimaster\ncd crewaimaster\npython -m venv venv\nsource venv/bin/activate\npip install -e .\n```\n\n## 🎬 Demo\n\n### Click below to watch a complete walkthrough of CrewAIMaster in action\n[![CrewAIMaster Demo](docs/gif/crewaimaster.gif)](https://youtu.be/osF9oTBa9IU)\n\n### 📸 Screenshots\n\n#### CLI Interface\n![CLI Interface](docs/images/crewaimaster-welcome.png)\n\n#### CrewAIMaster Providers\n![Crew Creation](docs/images/crewaimaster-providers.png)\n\n#### CrewAIMaster Help\n![Execution Dashboard](docs/images/crewaimaster-help.png)\n\n\n## 🏃 Quick Start\n\n### Prerequisites\n```bash\n# Install Python 3.10+\npython --version\n\n# Configure your LLM provider (see supported providers)\ncrewaimaster providers\n\n# Quick setup with OpenAI (most common)\ncrewaimaster providers --configure openai --api-key \"your-openai-key\" --model \"gpt-4\"\n```\n\n### 1. Create Your First Crew with AI Orchestration\n\n```bash\n# Create an intelligent crew using AI analysis\ncrewaimaster create \"Write a comprehensive market analysis report for electric vehicles in 2024\" --name electric_vehicles_market_analysis_crew\n```\n\n### 2. Execute the Crew\n\n```bash\n# Run the crew (requires configured LLM provider)\ncrewaimaster run electric_vehicles_market_analysis_crew\n\n# With additional context:\ncrewaimaster run electric_vehicles_market_analysis_crew --input \"Focus on Tesla, BMW, and Volkswagen specifically\"\n```\n\n### 3. Alternative Execution (Direct Script)\n\nGenerated crews can also be executed directly using environment variables:\n\n```bash\n# Navigate to the generated crew directory\ncd crews/electric_vehicles_market_analysis_crew\n\n# Run using standard environment variables\nexport OPENAI_API_KEY=\"your-openai-key\"\n./run.sh \"your input\"\n\n# Or run using CrewAIMaster-specific environment variables\nexport CREWAIMASTER_LLM_PROVIDER=\"openai\"\nexport CREWAIMASTER_LLM_MODEL=\"gpt-4\"\nexport CREWAIMASTER_LLM_API_KEY=\"your-openai-key\"\nexport CREWAIMASTER_LLM_BASE_URL=\"https://api.openai.com/v1\"\n```\n\n## 🔄 Development Workflow\n\n### Typical CrewAIMaster Workflow\n\n```mermaid\nflowchart LR\n    A[\"`**1. Task Definition**\n    Natural Language Task`\"] --\u003e B[\"`**2. AI Analysis**\n    🤖 Task Complexity\n    🎯 Agent Requirements\n    🛠️ Tool Selection`\"]\n    \n    B --\u003e C[\"`**3. Crew Creation**\n    👥 Agent Design\n    🔧 Tool Assignment\n    📋 Task Orchestration`\"]\n    \n    C --\u003e D[\"`**4. Execution**\n    🏃 Multi-Agent Coordination\n    🔄 Real-time Processing\n    📊 Progress Monitoring`\"]\n    \n    D --\u003e E[\"`**5. Results \u0026 Analytics**\n    📄 Output Generation\n    📈 Performance Metrics\n    💾 Persistent Storage`\"]\n    \n    E --\u003e F[\"`**6. Optimization**\n    🔧 Crew Modification\n    ⚡ Performance Tuning\n    📤 Export/Backup`\"]\n    \n    F --\u003e G[\"`**7. Reuse \u0026 Scale**\n    🔄 Crew Reusability\n    📚 Knowledge Building\n    🚀 Production Deployment`\"]\n\n    classDef stepStyle fill:#f9f9f9,stroke:#333,stroke-width:2px,color:#333\n    class A,B,C,D,E,F,G stepStyle\n```\n\n## 🏗️ Architecture\n\nCrewAIMaster follows a clean, layered architecture designed for intelligent multi-agent system creation and execution:\n\n```mermaid\nflowchart TD\n    %% User Entry Point\n    User[👤 User Input\u003cbr/\u003eNatural Language Task] --\u003e CLI[🖥️ CLI Interface\u003cbr/\u003ecrewaimaster create/run/providers]\n    \n    %% Configuration Layer\n    CLI --\u003e Config[⚙️ Configuration\u003cbr/\u003econfig.yaml\u003cbr/\u003eLLM Providers]\n    \n    %% AI Orchestration Core\n    CLI --\u003e MasterAgent[🧠 Master Agent\u003cbr/\u003eIntelligent Orchestrator]\n    \n    %% AI Analysis Pipeline\n    MasterAgent --\u003e TaskAnalyzer[📋 Task Analyzer\u003cbr/\u003e• Complexity Assessment\u003cbr/\u003e• Requirements Extraction\u003cbr/\u003e• Agent Planning]\n    \n    TaskAnalyzer --\u003e AgentDesigner[👥 Agent Designer\u003cbr/\u003e• Role Definition\u003cbr/\u003e• Tool Selection\u003cbr/\u003e• Capability Mapping]\n    \n    AgentDesigner --\u003e CrewOrchestrator[🎭 Crew Orchestrator\u003cbr/\u003e• Team Assembly\u003cbr/\u003e• Process Selection\u003cbr/\u003e• Workflow Design]\n    \n    %% Core Generation Engine\n    CrewOrchestrator --\u003e CrewDesigner[🔧 Crew Designer\u003cbr/\u003eFile-Based Generator]\n    Config --\u003e CrewDesigner\n    \n    CrewDesigner --\u003e FileGen[📁 File Generator\u003cbr/\u003e• Project Structure\u003cbr/\u003e• Python Modules\u003cbr/\u003e• YAML Configs]\n    \n    %% Output Generation\n    FileGen --\u003e GeneratedFiles{📄 Generated Crew Project}\n    \n    %% Generated Project Structure\n    GeneratedFiles --\u003e AgentYAML[agents.yaml\u003cbr/\u003eAgent Definitions]\n    GeneratedFiles --\u003e TaskYAML[tasks.yaml\u003cbr/\u003eTask Specifications]\n    GeneratedFiles --\u003e CrewPY[crew.py\u003cbr/\u003eCrewAI Implementation]\n    GeneratedFiles --\u003e MainPY[main.py\u003cbr/\u003eExecution Entry Point]\n    \n    %% Execution Runtime\n    MainPY --\u003e CrewAI[🚀 CrewAI Runtime\u003cbr/\u003eMulti-Agent Execution]\n    \n    CrewAI --\u003e AgentA[🤖 Agent A\u003cbr/\u003eSpecialized Role]\n    CrewAI --\u003e AgentB[🤖 Agent B\u003cbr/\u003eSpecialized Role]\n    CrewAI --\u003e AgentC[🤖 Agent C\u003cbr/\u003eSpecialized Role]\n    \n    %% Tool Integration\n    AgentA --\u003e Tools[🛠️ Tool Registry\u003cbr/\u003e• Web Search\u003cbr/\u003e• File Operations\u003cbr/\u003e• Code Execution\u003cbr/\u003e• Custom Tools]\n    AgentB --\u003e Tools\n    AgentC --\u003e Tools\n    \n    %% LLM Integration\n    Config --\u003e LLMProvider[🔗 LLM Provider\u003cbr/\u003e• OpenAI\u003cbr/\u003e• Anthropic\u003cbr/\u003e• Google\u003cbr/\u003e• Custom APIs]\n    LLMProvider --\u003e AgentA\n    LLMProvider --\u003e AgentB\n    LLMProvider --\u003e AgentC\n    LLMProvider --\u003e MasterAgent\n    \n    %% Memory \u0026 Knowledge\n    CrewAI --\u003e Memory[🧠 Memory System\u003cbr/\u003e• Agent Memory\u003cbr/\u003e• Shared Context\u003cbr/\u003e• Knowledge Base]\n    \n    %% Safety \u0026 Guardrails\n    Tools --\u003e Guardrails[🛡️ Guardrails\u003cbr/\u003e• Safety Checks\u003cbr/\u003e• Content Filtering\u003cbr/\u003e• Validation]\n    \n    %% Final Output\n    CrewAI --\u003e Results[📊 Results\u003cbr/\u003eTask Completion\u003cbr/\u003eGenerated Content]\n    \n    %% Styling\n    classDef userLayer fill:#e8f5e8,stroke:#1b5e20,stroke-width:3px,color:#000\n    classDef cliLayer fill:#e1f5fe,stroke:#01579b,stroke-width:2px,color:#000\n    classDef aiLayer fill:#f3e5f5,stroke:#4a148c,stroke-width:2px,color:#000\n    classDef coreLayer fill:#fff8e1,stroke:#ff8f00,stroke-width:2px,color:#000\n    classDef fileLayer fill:#fce4ec,stroke:#880e4f,stroke-width:2px,color:#000\n    classDef runtimeLayer fill:#fff3e0,stroke:#e65100,stroke-width:2px,color:#000\n    classDef toolLayer fill:#f1f8e9,stroke:#33691e,stroke-width:2px,color:#000\n    \n    class User userLayer\n    class CLI,Config cliLayer\n    class MasterAgent,TaskAnalyzer,AgentDesigner,CrewOrchestrator aiLayer\n    class CrewDesigner,FileGen,LLMProvider coreLayer\n    class GeneratedFiles,AgentYAML,TaskYAML,CrewPY,MainPY fileLayer\n    class CrewAI,AgentA,AgentB,AgentC,Memory,Results runtimeLayer\n    class Tools,Guardrails toolLayer\n```\n\n### 🔄 Data Flow Explanation\n\n1. **User Input**: Natural language task description via CLI\n2. **AI Analysis**: Master Agent analyzes complexity and requirements\n3. **Intelligent Design**: AI agents design optimal crew composition\n4. **Code Generation**: Automated creation of CrewAI project files\n5. **Execution**: Generated crew runs with real-time coordination\n6. **Results**: Task completion with generated content and insights\n\n### 🏛️ Architecture Overview\n\nCrewAIMaster's architecture is designed for scalability, modularity, and intelligent automation:\n\n#### 🎯 **User Interface Layer**\n- **CLI Interface**: Rich terminal experience with typer and rich libraries\n- **Command Processing**: Handles user commands and provides interactive feedback\n- **Input Validation**: Ensures commands are properly formatted and validated\n\n#### 🤖 **AI Orchestration Layer** (Core Innovation)\n- **MasterAgentCrew**: Main orchestrator using AI agents for intelligent decision-making\n- **TaskAnalyzerAgent**: Advanced NLP analysis of user tasks and requirements\n- **AgentDesignerAgent**: Intelligent design of agents based on task requirements\n- **CrewOrchestratorAgent**: Optimizes crew composition and execution strategies\n\n#### ⚙️ **Core Processing Layer**\n- **CrewDesigner**: Handles CrewAI integration and agent instantiation\n- **TaskAnalyzer**: Legacy fallback for task analysis with pattern matching\n\n#### 🛠️ **Tool Ecosystem**\n- **Tool Registry**: Centralized management of all available tools\n- **Available Tools**: Comprehensive library of built-in and custom tools\n- **Guardrails**: Safety and validation systems for secure operation\n\n#### 🔄 **Execution Engine**\n- **CrewAI Engine**: Core execution engine for running multi-agent crews\n- **Agent Memory**: Sophisticated memory management for agent learning and context\n\n### 🔄 Data Flow\n\n1. **User Input** → CLI processes commands and validates input\n2. **AI Analysis** → MasterAgentCrew analyzes task using specialized AI agents\n3. **Crew Creation** → CrewDesigner instantiates agents with appropriate tools\n4. **Execution** → CrewAI Engine runs the crew with real-time monitoring\n\n## 🛠️ Configuration\n\n### LLM Provider Setup\n\nCrewAIMaster uses a `.crewaimaster/config.yaml` configuration file for all settings. Environment variables are **no longer supported** - all configuration must be done via CLI commands or direct config file editing.\n\n#### 📋 **View Available Providers**\n```bash\n# See all supported providers and configuration examples\ncrewaimaster providers\n```\n\n#### 🚀 **CLI Configuration (All Providers)**\n\nConfigure any supported provider using the CLI:\n\n**OpenAI:**\n```bash\ncrewaimaster providers --configure openai --api-key \"your-openai-key\" --model \"gpt-4\"\n# Automatically sets base_url to https://api.openai.com/v1\n```\n\n**Anthropic:**\n```bash\ncrewaimaster providers --configure anthropic --api-key \"your-anthropic-key\" --model \"claude-3-sonnet-20240229\"\n# Automatically sets base_url to https://api.anthropic.com/v1\n```\n\n**Google:**\n```bash\ncrewaimaster providers --configure google --api-key \"your-google-key\" --model \"gemini-pro\"\n# Automatically sets base_url to https://generativelanguage.googleapis.com/v1beta\n```\n\n**DeepSeek:**\n```bash\ncrewaimaster providers --configure deepseek --api-key \"your-deepseek-key\" --model \"deepseek-chat\"\n# Automatically sets base_url to https://api.deepseek.com/v1\n```\n\n**Custom Provider:**\n```bash\ncrewaimaster providers --configure custom --api-key \"your-key\" --base-url \"https://api.example.com/v1\" --model \"gpt-4o-mini\"\n# Requires explicit base_url for custom endpoints\n```\n\n## 🤝 Contributing\n\nWe welcome contributions! Here's how to get started:\n\n1. Fork the repository\n2. Create a feature branch: `git checkout -b feature/amazing-feature`\n3. Make your changes and add tests\n4. Run tests: `pytest tests/`\n5. Commit changes: `git commit -m 'Add amazing feature'`\n6. Push to branch: `git push origin feature/amazing-feature`\n7. Open a Pull Request\n\n### Development Setup\n\n```bash\n# Clone and setup development environment\ngit clone https://github.com/VishApp/crewaimaster\ncd crewaimaster\n\n# Install development dependencies\npip install -e .\n```\n\n## 📄 License\n\nCrewAIMaster is released under the MIT License. See [LICENSE](LICENSE) for details.\n\n## 🙏 Acknowledgments\n\n- [CrewAI](https://github.com/joaomdmoura/crewAI) - Core multi-agent framework\n- [LangChain](https://github.com/langchain-ai/langchain) - LLM integration tools  \n- [Sentence Transformers](https://github.com/UKPLab/sentence-transformers) - Text embeddings\n- [FAISS](https://github.com/facebookresearch/faiss) - Vector similarity search\n\n## 🔗 Links\n\n- [GitHub Repository](https://github.com/VishApp/crewaimaster)\n- [PyPI Package](https://pypi.org/project/crewaimaster)\n---\n\n**Built with ❤️ for the AI community**","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvishapp%2Fcrewaimaster","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvishapp%2Fcrewaimaster","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvishapp%2Fcrewaimaster/lists"}