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A simple demonstration agent using the ReACT methodology for analyzing and executing tasks.

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Hello World Agent 🌟

A powerful, extensible agent framework leveraging ReACT methodology for autonomous task execution and human-in-the-loop collaboration.

🚀 Quick Start

  1. Install the package:

    pip install hello_agent
  2. Set up your environment variables:

    Create a .env file in the root directory of your project and add the following:

    # OpenRouter API Key
    # Get your API key from https://openrouter.ai/
    OPENROUTER_API_KEY=your_api_key_here
    
    # Optional: LLM Model Settings
    # Default models are set in agents.yaml, but can be overridden here
    # RESEARCHER_MODEL=anthropic/claude-2
    # EXECUTOR_MODEL=anthropic/claude-2
    # ANALYZER_MODEL=anthropic/claude-2
    
    # Optional: Debug Mode
    # Set to true to enable additional logging
    # DEBUG=false
    
    # Optional: HITL Settings
    # Enable/disable human-in-the-loop by default
    # Can be overridden with --hitl flag
    # HITL_ENABLED=false
  3. Run the agent:

    python agent/main.py

🎯 Key Features

Feature Description
ReACT Methodology Structured reasoning and action framework for intelligent task execution
Multi-Modal Tasks Research, execution, and analysis capabilities
Streaming Responses Real-time output with progress tracking
Human (HITL) Integration Optional human validation at key decision points
Extensible Tools Modular architecture for custom tool integration
Advanced LLM Support Powered by OpenRouter API for state-of-the-art language models

🔧 Core Capabilities

Capability Details
Research Information gathering, analysis, and synthesis
Execution Task implementation with validation and quality checks
Analysis Performance metrics, optimization, and recommendations
HITL Human validation for critical decisions
Progress Tracking Real-time status updates and metrics
Error Recovery Robust error handling and state preservation

🎮 Control Modes

Mode Description
Autonomous Self-directed task execution with ReACT methodology
HITL Interactive mode with human validation points
Streaming Real-time response processing and updates

🛠️ Technical Stack

Component Technology
Core Framework Python 3.8+
LLM Integration OpenRouter API
Task Management CrewAI
Configuration YAML-based
API REST with OpenAPI spec
Documentation Markdown + Examples

📊 Performance Metrics

Metric Target
Response Time < 2s for standard operations
Streaming Latency < 100ms
Task Success Rate > 95%
HITL Integration < 5s response time

🔐 Security Features

Feature Implementation
Authentication OpenRouter API key
Configuration Environment variables
Rate Limiting 100 requests/hour
Access Control Role-based permissions

📚 Documentation

Comprehensive guides available for all aspects:

Guide Content
User Guide Getting started and basic usage
Templates Customizing agent responses
Tools Extending agent capabilities
Configuration System setup and options
Advanced Complex implementations
Memory/Storage Data management
HITL Human integration guide

🎯 Use Cases

Industry Applications
Research Literature review, data analysis
Development Code generation, testing
Operations System monitoring, optimization
Support Customer service, documentation
Analysis Performance metrics, reporting

🔄 Integration Options

Method Description
CLI Command-line interface
Python API Direct library integration
REST API HTTP endpoints
AI Plugin OpenAI plugin compatibility

🌐 Ecosystem Support

Component Status
PyPI Package ✅ Available
Documentation ✅ Comprehensive
Examples ✅ Included
Community 🚀 Growing

📈 Future Roadmap

Feature Status
Multi-Agent Support 🚧 In Development
Advanced Analytics 🎯 Planned
GUI Interface 💡 Proposed
Cloud Deployment 🎯 Planned

🤝 Contributing

Join our community! We welcome contributions of all kinds:

  • 🐛 Bug Reports
  • 💡 Feature Suggestions
  • 🔧 Code Contributions
  • 📚 Documentation Improvements

📄 License

MIT License - See LICENSE for details.

🙏 Acknowledgments


Made by rUv with 💫 for the AI community

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