UltronAI

🚀 Overview

UltronAI is a sophisticated General-Purpose Artificial General Intelligence (AGI) system that leverages multiple specialized AI agents to execute complex tasks intelligently. Built with a modular, state-driven architecture, it can handle diverse tasks ranging from system health checks to portfolio creation, web development, data analysis, and more .

Key Features

• 🤖 Multi-Agent Architecture: 9 specialized agents working in coordination
• 🧠 AI-Powered Planning: Intelligent task decomposition and execution planning
• 📊 Rich Terminal UI: Beautiful, informative interface with progress tracking
• 🔄 State-Driven Execution: Persistent state management across task execution
• 🛡️ Error Resilience: Robust error handling and recovery mechanisms
• 📁 Document Processing: PDF reading and content extraction capabilities
• 🌐 Web Research: Comprehensive web search and synthesis capabilities
• 💻 Code Generation: Advanced code generation with quality assessment

🏗️ Architecture

Core Components

UltronAI/
├── main.py                 # Main entry point and orchestration
├── shared_state.py         # State management and persistence
├── requirements.txt        # Dependencies
├── Agents/                # Specialized agent modules
│   ├── planner_agent.py   # Task planning and decomposition
│   ├── coder_agent.py     # Code generation and refinement
│   ├── file_handler_agent.py # File operations and project structure
│   ├── shell_executer_agent.py # System operations and shell commands
│   ├── web_search.py      # Web research and synthesis
│   ├── error_resolver.py  # Error analysis and recovery
│   ├── document_reader_agent.py # PDF and document processing
│   ├── environment_check_agent.py # System dependency checking
│   ├── human_intervention_agent.py # User interaction handling
│   └── prompt_refiner.py  # Task refinement and optimization
└── projects/              # Generated project outputs

Agent Specialization

Agent	Primary Function	Capabilities
Planner Agent	Task decomposition and planning	Goal analysis, quality assessment, execution planning
Coder Agent	Code generation and refinement	Content analysis, structure design, code review
File Handler Agent	File operations and project structure	Project detection, backup protection, intelligent naming
Shell Executer Agent	System operations and shell commands	Platform-aware commands, system health checks
Web Search Agent	Web research and synthesis	Multi-source search, analysis, synthesis
Error Resolver Agent	Error analysis and recovery	Root cause analysis, fix strategy generation
Document Reader Agent	Document processing	PDF reading, content extraction
Environment Check Agent	System dependency verification	Tool availability checking
Human Intervention Agent	User interaction handling	Help requests, user guidance

🛠️ Installation

Prerequisites

• Python 3.8 or higher
• macOS, Linux, or Windows
• Internet connection for AI model access

Setup

1. Clone the repository

   git clone <repository-url>
   cd UltronAI

2. Create virtual environment

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Set up environment variables

   cp .env.example .env
   # Edit .env with your API keys

5. Run the system

   python main.py

🎯 Usage Examples

System Health Check

python main.py
# Enter: "check my system health and report the status through terminal"

Portfolio Creation from Resume

python main.py
# Enter: "my resume is in desktop named 'resume.pdf', read it and make a portfolio page"

Web Development

python main.py
# Enter: "create a responsive landing page for a tech startup"

Data Analysis

python main.py
# Enter: "analyze sales data and create visualizations"

🔧 Technical Details

State Management

The system uses a sophisticated state management approach through the SharedState class:

class SharedState:
    def __init__(self, original_task: str):
        self.original_task: str = original_task
        self.current_plan: List[Dict[str, Any]] = []
        self.project_directory: Optional[Path] = None
        self.created_files: List[str] = []
        self.generated_code: Dict[str, str] = {}
        self.document_content: Optional[str] = None
        # ... additional state properties

Agent Communication Protocol

Agents communicate through a standardized JSON format:

{
    "agent": "agent_name",
    "description": "specific command or instruction"
}

Execution Flow

1. Task Input → User provides task description
2. Task Refinement → Prompt refiner optimizes the task
3. State Initialization → SharedState created with task context
4. Document Processing → If documents mentioned, read and extract content
5. Planning Phase → Planner agent creates execution plan
6. Execution Loop → Agents execute tasks in sequence
7. Quality Assessment → Output quality evaluated
8. Refinement → If needed, code is refined and improved
9. Completion → Final output delivered

Error Handling

The system implements multiple layers of error handling:

• Pre-flight Checks: Environment and dependency verification
• Execution Monitoring: Real-time error detection
• Error Analysis: Root cause identification
• Recovery Strategies: Automatic fix plan generation
• Human Intervention: User guidance when automatic recovery fails

🧠 AI Models and Capabilities

Primary AI Model

• Groq DeepSeek R1 Distill Llama 70B: High-performance, fast inference model

Agent Intelligence Features

Planner Agent

• Goal Analysis: Extracts ultimate goals and success criteria
• Quality Assessment: Evaluates output quality (1-10 scale)
• Execution Planning: Creates detailed step-by-step plans
• Refinement Planning: Generates improvement strategies

Coder Agent

• Content Analysis: Parses unstructured text into structured data
• Structure Design: Creates optimal layouts and designs
• Code Generation: Produces production-ready code
• Code Review: Self-assessment and improvement
• Iterative Refinement: Continuous quality improvement

File Handler Agent

• Project Type Detection: Automatically identifies project types
• Intelligent Structure Creation: Creates appropriate project structures
• Backup Protection: Prevents data loss through backup mechanisms
• Content Awareness: Reads and analyzes existing files

Web Search Agent

• Multi-Source Search: Performs comprehensive web searches
• Source Evaluation: Assesses credibility and relevance
• Information Synthesis: Creates actionable summaries
• Analysis: Provides detailed insights and recommendations

📊 Performance and Scalability

Performance Metrics

• Response Time: Typically 30-60 seconds for complex tasks
• Accuracy: High-quality output with iterative refinement
• Reliability: Robust error handling and recovery
• Scalability: Modular architecture supports easy expansion

Resource Usage

• Memory: Efficient state management with cleanup
• CPU: Optimized for multi-agent coordination
• Network: Minimal API calls with intelligent caching
• Storage: SQLite-based persistent storage

🔒 Security and Safety

Security Features

• Command Validation: Prevents dangerous shell commands
• File Protection: Backup mechanisms prevent data loss
• Input Sanitization: Validates all user inputs
• Error Isolation: Errors don't propagate across agents

Safety Measures

• Timeout Protection: Prevents infinite loops
• Resource Limits: Prevents excessive resource usage
• Error Recovery: Graceful degradation on failures
• User Confirmation: Critical operations require user approval

🧪 Testing and Quality Assurance

Testing Strategy

• Unit Tests: Individual agent functionality
• Integration Tests: Multi-agent coordination
• End-to-End Tests: Complete task execution
• Error Scenario Tests: Error handling and recovery

Quality Metrics

• Code Quality: Automated code review and scoring
• Output Quality: 1-10 scale assessment
• User Satisfaction: Feedback collection and analysis
• Performance Monitoring: Response time and resource usage

🚀 Advanced Features

Content-Aware Processing

• Document Reading: PDF parsing and content extraction
• Structured Data: Intelligent parsing of unstructured content
• Context Preservation: Maintains context across operations
• Content Integration: Uses real content instead of placeholders

Intelligent Planning

• Goal-Oriented: Focuses on ultimate objectives
• Quality-Driven: Prioritizes output quality
• Adaptive: Adjusts plans based on progress
• Iterative: Continuous improvement cycles

Rich User Interface

• Progress Tracking: Real-time execution progress
• Status Updates: Detailed status information
• Error Reporting: Clear error messages and solutions
• Output Display: Beautiful formatting of results

🤝 Contributing

Development Setup

1. Fork the repository
2. Create a feature branch
3. Implement your changes
4. Add tests for new functionality
5. Submit a pull request

Code Standards

• PEP 8: Python code style guidelines
• Type Hints: Comprehensive type annotations
• Documentation: Detailed docstrings and comments
• Error Handling: Robust error management

Testing Guidelines

• Unit Tests: Test individual functions and methods
• Integration Tests: Test agent interactions
• End-to-End Tests: Test complete workflows
• Performance Tests: Monitor resource usage

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Groq: For providing high-performance AI inference
• DeepSeek: For the powerful language model
• Rich: For the beautiful terminal UI framework
• Agno: For the agent framework and utilities

📞 Support

For support, questions, or feature requests:

• Issues: Create an issue on GitHub
• Discussions: Use GitHub Discussions
• Documentation: Check the inline code documentation
• Examples: See the projects/ directory for examples

---

UltronAI Multi-Agent System - Empowering intelligent task execution through coordinated AI agents. 🚀

---

Created with ❤️ by Amit Anand