Hyper Neuro Graph 🧠🚀

A Multi-Agent SDLC Development Platform & Framework

🌟 Overview

Hyper Neuro Graph is a foundation platform for building sophisticated multi-agent software development lifecycle (SDLC) systems. This repository provides the initial setup, framework, and tools for development teams to collaborate on creating a hierarchical network of AI agents that automate various aspects of software development.

🎯 Project Vision

Build a comprehensive tree-like agent architecture where AI agents collaborate to handle design, development, testing, deployment, and monitoring of software systems. The planned architecture will support 53+ specialized agents organized in a hierarchical structure.

🏗️ Current State

• ✅ Foundation Setup: Core infrastructure and framework ready
• ✅ Development Environment: Configured for team collaboration
• ✅ Agent Framework: Ready for agent development and integration
• 🚧 Agent Network: To be built by development team
• 🚧 Specialized Agents: Planned 53+ agents to be implemented

🏗️ Planned Architecture

Hybrid SDLC Tree Architecture

graph TB
    SUPER[🎯 SDLC Supervisor]
    
    ARCH[🏛️ Architect Controller]
    CODE[💻 Code Controller] 
    TEST[🧪 Test Controller]
    REV[👁️ Review Controller]
    DEPLOY[🚀 Deploy Controller]
    MON[📊 Monitor Controller]
    
    SUPER --> ARCH
    SUPER --> CODE
    SUPER --> TEST
    SUPER --> REV
    SUPER --> DEPLOY
    SUPER --> MON
    
    subgraph "To Be Developed"
        ARCH -.-> A1[System Designer]
        ARCH -.-> A2[Pattern Analyst]
        CODE -.-> C1[Code Generator]
        CODE -.-> C2[Refactor Expert]
        TEST -.-> T1[Unit Tester]
        TEST -.-> T2[Integration Tester]
    end

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📋 Planned Agent Network

• 1 Supervisor Agent: Central orchestrator (to be developed)
• 6 Domain Controllers: High-level coordinators (to be developed)
• 46+ Specialized Agents: Domain experts (to be developed by team)
• Total Target: 53+ intelligent agents

🚀 Developer Onboarding

📋 Prerequisites

• Python: 3.12 or higher
• Git: Latest version
• API Keys: Anthropic API key for Claude models
• IDE: VS Code or PyCharm recommended

⚡ Quick Setup

1. Clone and Navigate

   git clone <repository-url>
   cd hyper-neuro-graph

2. Environment Setup

   # Create virtual environment
   python -m venv .venv
   source .venv/bin/activate  # Windows: .venv\Scripts\activate
   
   # Install dependencies
   pip install -r requirements.txt

3. Configuration

   # Copy environment template
   cp .env.example .env
   
   # Edit .env with your API keys
   nano .env  # or use your preferred editor

4. Verify Setup

   # Test environment
   ./diagnose_env.sh
   
   # Start the system
   python run.py

📖 How Things Work

🏗️ System Architecture

1. Core Components

Hyper Neuro Graph
├── 🎯 Neuro SAN Server     # Orchestration engine
├── 🌐 Agent Registry       # HOCON configurations
├── 🛠️ Toolbox             # Shared utilities
├── 📡 Communication Layer  # Agent messaging
└── 📊 Monitoring Studio    # Real-time visibility

2. Agent Lifecycle

sequenceDiagram
    participant Dev as Developer
    participant Reg as Registry
    participant NS as Neuro SAN
    participant Agent as Agent
    
    Dev->>Reg: 1. Define Agent (HOCON)
    Dev->>Agent: 2. Implement Logic (Python)
    Reg->>NS: 3. Register Agent
    NS->>Agent: 4. Initialize & Activate
    Agent->>NS: 5. Report Status
    NS->>Dev: 6. Monitor via Studio

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3. Communication Flow

• Orchestration: Neuro SAN manages agent coordination
• Messaging: Async communication between agents
• Workflows: LangGraph handles complex multi-step processes
• Monitoring: Real-time visibility in Neuro SAN Studio

🔧 Development Workflow

Phase 1: Foundation (Current)

# 1. Environment setup and validation
./diagnose_env.sh

# 2. Basic system verification
python run.py --validate

# 3. Access studio interface
# Navigate to http://localhost:8080

Phase 2: Agent Development

# 1. Create agent configuration
nano registries/my_agent.hocon

# 2. Implement agent logic
nano apps/my_agent/agent.py

# 3. Test agent locally
python -m tests.test_my_agent

# 4. Register and deploy
python run.py --register my_agent

🛠️ Development Best Practices

📋 Agent Development Guidelines

1. Configuration First (HOCON)

# registries/example_agent.hocon
{
    "agent_name": "example_agent",
    "llm_config": {
        "model_name": "claude-3-5-sonnet-20241022",
        "provider": "anthropic",
        "temperature": 0.7,
        "max_tokens": 4096
    },
    "instructions": "You are a specialized agent for...",
    "tools": ["calculator", "web_search"],
    "down_chains": ["sub_agent_1", "sub_agent_2"]
}

2. Modular Implementation

# apps/example_agent/agent.py
class ExampleAgent:
    def __init__(self, config):
        self.config = config
        self.llm = self._setup_llm()
        
    async def process_task(self, task):
        # Agent-specific logic here
        return result
        
    def _setup_llm(self):
        # LLM initialization
        pass

3. Testing Strategy

# tests/test_example_agent.py
import pytest
from apps.example_agent.agent import ExampleAgent

# Test configuration setup
test_config = {
    "agent_name": "test_agent",
    "llm_config": {
        "model_name": "claude-3-5-sonnet-20241022",
        "provider": "anthropic",
        "temperature": 0.1
    },
    "capabilities": ["analyze", "generate", "validate"]
}

# Test task setup
test_task = {
    "type": "analysis",
    "content": "Analyze this sample code structure",
    "requirements": ["code_review", "suggestions"]
}

def test_agent_initialization():
    agent = ExampleAgent(test_config)
    assert agent.config is not None
    
async def test_task_processing():
    agent = ExampleAgent(test_config)
    result = await agent.process_task(test_task)
    assert result.success is True

🎯 Best Practices Checklist

✅ Code Quality

• Follow Python PEP 8 style guide
• Add comprehensive docstrings
• Implement error handling
• Write unit tests (>80% coverage)
• Use type hints throughout

✅ Agent Design

• Single responsibility principle
• Clear input/output contracts
• Proper logging and monitoring
• Graceful error handling
• Configurable behavior

✅ Integration

• HOCON configuration follows template
• Proper registration in manifest
• Communication protocols implemented
• Studio visibility enabled
• Documentation updated

🔧 Development Workflow

1. Planning Phase

# Review architecture document
cat HYBRID_SDLC_TREE_ARCHITECTURE.md

# Plan agent responsibilities
# Define interfaces and contracts
# Create development timeline

2. Implementation Phase

# Create feature branch
git checkout -b feature/agent-name

# Set up agent structure
mkdir -p apps/agent-name
mkdir -p tests/agent-name
touch registries/agent-name.hocon

# Implement and test
# Follow TDD approach

3. Integration Phase

# Test locally
python run.py --test-mode

# Integration tests
./run_integration_tests.sh

# Code review and merge
git push origin feature/agent-name
# Create pull request

🧪 Testing & Validation

🔧 Environment Testing

# Comprehensive environment check
./diagnose_env.sh

# Dependency validation
./fix_dependencies.sh --check-only

# Configuration validation  
python run.py --validate-config

🧪 Agent Testing

# Unit tests
python -m pytest tests/ -v

# Integration tests
python -m pytest tests/integration/ -v

# Load testing
python -m tests.load_test --agents 10

📊 Performance Monitoring

# Start with monitoring
python run.py --monitor

# Check metrics
curl http://localhost:8080/metrics

# View logs
tail -f logs/agent_thinking.txt

🏗️ Project Structure & Guidelines

📁 Directory Organization

hyper-neuro-graph/
├── 📁 apps/                    # Individual agent implementations
│   ├── conscious_assistant/    # Conversational AI interface
│   ├── cruse/                  # Multi-agent web client
│   ├── log_analyzer/           # Log analysis tools
│   ├── wwaw/                   # Web Agent Network Builder
│   ├── agents/                 # Additional agent implementations
│   └── example_agent/          # Example template agent
├── 📁 coded_tools/            # Custom tools and utilities
│   └── advanced_calculator/    # Calculator tool implementation
├── 📁 registries/             # HOCON agent configurations
│   ├── six_thinking_hats.hocon # Six thinking hats agent config
│   ├── hybrid_architect_controller.hocon # Architect controller config
│   └── manifest.hocon          # Agent registry manifest
├── 📁 servers/                # Server components and APIs
│   ├── a2a/                   # Agent-to-agent communication
│   └── mcp/                   # MCP (Model Context Protocol) server
├── 📁 deploy/                 # Deployment configurations
│   ├── Dockerfile             # Container definition
│   ├── build.sh               # Build script
│   ├── run.sh                 # Run script
│   ├── entrypoint.sh          # Container entrypoint
│   └── logging.json           # Logging configuration
├── 📁 tests/                  # Test suites
│   ├── apps/                  # Application tests
│   └── coded_tools/           # Tool tests
├── 📁 docs/                   # Documentation
│   ├── user_guide.md          # User guide
│   ├── api_key.md             # API key documentation
│   ├── examples.md            # Examples and tutorials
│   ├── comparative_analysis.md # Comparative analysis
│   ├── examples/              # Example configurations
│   └── images/                # Documentation images
├── 📁 toolbox/                # Shared tools and resources
│   └── toolbox_info.hocon     # Tool configurations
├── 📁 logs/                   # Application logs
│   ├── agent_thinking.txt     # Agent conversation logs
│   └── thinking_dir/          # Detailed agent logs
├── 📄 HYBRID_SDLC_TREE_ARCHITECTURE.md # Complete architecture design
├── 📄 run.py                  # Main entry point
├── 📄 pyproject.toml          # Project configuration
├── 📄 requirements.txt        # Python dependencies
├── 📄 requirements-build.txt  # Build dependencies
├── 📄 Makefile                # Build automation
├── 📄 .env.example           # Environment template
├── 📄 .gitignore             # Git ignore rules
├── 🔧 diagnose_env.sh         # Environment diagnostic script
└── 🔧 fix_dependencies.sh     # Dependency fix script

📋 Naming Conventions

• Agents: snake_case (e.g., code_generator, test_runner)
• Classes: PascalCase (e.g., CodeGenerator, TestRunner)
• Functions: snake_case (e.g., process_task, validate_input)
• Files: snake_case.py (e.g., agent_utils.py, config_loader.py)

🔧 Configuration Management

# Environment variables
cp .env.example .env
# Edit with actual values

# Agent configurations
# Use HOCON format for all configs
# Follow existing templates
# Validate before committing

📚 Resources & Learning

📖 Essential Reading

• Architecture Guide: Complete system design
• Agent Development Guide: Step-by-step agent creation
• API Reference: Complete API documentation
• Best Practices: Code quality guidelines

🔗 External Resources

• Neuro SAN Documentation
• LangGraph Guide
• HOCON Format
• Anthropic API

🤝 Contributing

📋 Contribution Workflow

1. Fork & Clone: Get your own copy
2. Branch: Create feature branch
3. Develop: Follow best practices
4. Test: Comprehensive testing
5. Document: Update relevant docs
6. Submit: Create pull request

🔍 Code Review Checklist

• Code follows style guidelines
• Tests pass and coverage >80%
• Documentation updated
• No breaking changes
• Performance impact assessed

🛠️ Troubleshooting

🔧 Common Issues

Environment Problems

# Diagnose issues
./diagnose_env.sh

# Fix dependencies
./fix_dependencies.sh

# Reset environment
rm -rf .venv
python -m venv .venv
pip install -r requirements.txt

Agent Registration Issues

# Validate HOCON syntax
python -c "import pyhocon; pyhocon.ConfigFactory.parse_file('registries/agent.hocon')"

# Check agent manifest
python run.py --list-agents

# Refresh registry
python run.py --refresh-registry

Communication Issues

# Check port availability
netstat -an | grep 8080

# Restart services
./stop_neuro_san.sh
./start_neuro_san.sh

# Monitor connections
tail -f logs/communication.log

📞 Getting Help

• Documentation: Check /docs directory

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Ready to build the future of AI-powered software development? 🚀

Start with the setup guide above and join the development team in creating revolutionary multi-agent systems!