HarmonyCode v3.1.0

The AI Collaboration Framework That Prevents Echo Chambers - Now with Persistent Identity!

HarmonyCode v3.1.0 is the unified platform that combines:

🎵 Real-time collaboration (from HarmonyCode v1)
🎼 Advanced orchestration (from Claude-Flow)
🛡️ Anti-echo-chamber protection (preventing AI groupthink)
🆔 Persistent identity system (NEW in v3.1.0!)
🔒 Atomic task locking (NEW in v3.1.0!)

What's New in v3.1.0?

Persistent Identity System - Agents maintain their identity across sessions and role changes:

Unique agent IDs that never change
Authentication tokens for seamless reconnection
Complete history tracking
Role flexibility without identity loss

Race Condition Fixes - Atomic locking prevents task claim conflicts:

5-second lock timeout
Exclusive task claims
No more duplicate work

Improved CLI UX - Shorter commands and better experience:

Use hc instead of harmonycode
Command suggestions for typos
Enhanced help system

Why v3.0.0?

Previous versions proved that AI agents can collaborate, but they also revealed a critical flaw: artificial consensus. When AIs work together, they tend to agree too quickly, creating echo chambers that lead to poor decisions.

HarmonyCode v3.0.0 solves this by enforcing intellectual diversity at every level.

🚀 Quick Start

Installation

npm install -g harmonycode@latest

Create Your First Project

# Initialize project with anti-echo-chamber enabled
hc init my-ai-team

# Start the collaboration server
cd my-ai-team
hc server

# In another terminal, register and join as an agent
hc register alice
hc join alice --role researcher

# Or use the short alias
hc j alice

# Start a swarm with diversity enforcement
hc swarm "Design a user authentication system" --anti-echo

🎯 Key Features

1. Real-Time Collaboration with Diversity

# Multiple agents collaborate with enforced perspectives
harmonycode join agent1 --role coder --perspective optimist
harmonycode join agent2 --role reviewer --perspective skeptic
harmonycode join agent3 --role architect --perspective pragmatist

2. SPARC Development Modes

# Run specialized AI modes with built-in diversity
harmonycode sparc tdd "Build user service"
harmonycode sparc researcher "Analyze security options" --require-evidence
harmonycode sparc architect "Design microservices" --min-perspectives 3

3. Swarm Orchestration

# Launch AI swarms that avoid groupthink
harmonycode swarm "Build e-commerce platform" \
  --strategy distributed \
  --max-agents 10 \
  --disagreement-quota 0.3 \
  --evidence-threshold 0.7

4. Anti-Echo-Chamber Enforcement

Disagreement Quotas: 30% of agents must provide dissenting views
Evidence Requirements: Claims need supporting data
Perspective Rotation: Agents switch viewpoints to avoid entrenchment
Diversity Metrics: Real-time monitoring of intellectual diversity

📊 How It Works

Traditional AI Collaboration (Echo Chamber)

AI-1: "Let's use MongoDB"
AI-2: "I agree, MongoDB is perfect"
AI-3: "Yes, MongoDB for sure"
Result: Quick consensus, potential blind spots

HarmonyCode v3.0.0 (Enforced Diversity)

AI-1: "Let's use MongoDB"
AI-2: ❌ Blocked: "Must provide different perspective"
AI-2: "MongoDB has scalability issues for our use case..."
AI-3: "PostgreSQL offers better ACID compliance..."
AI-4: "Here's benchmark data comparing both..."
Result: Evidence-based decision with 78% confidence

🛠️ Architecture

harmonycode-v3/
├── core/              # Real-time WebSocket collaboration
├── orchestration/     # SPARC modes and task management
├── diversity/         # Anti-echo-chamber enforcement
├── cli/              # Unified command interface
└── ui/               # Web dashboard (coming soon)

Core Components

WebSocket Layer (from HarmonyCode v1)
- Real-time message passing
- Conflict resolution
- File synchronization
Orchestration Engine (from Claude-Flow)
- Task decomposition and assignment
- SPARC mode management
- Memory persistence
Diversity Middleware (from Anti-Echo-Chamber)
- Perspective tracking
- Echo pattern detection
- Intervention enforcement

🎮 CLI Commands

Project Management

harmonycode init <project>      # Initialize new project
harmonycode server              # Start collaboration server
harmonycode monitor             # View real-time metrics

Agent Management

harmonycode join <name>         # Join as an agent
harmonycode agent spawn <type>  # Spawn specialized agent
harmonycode agent list          # List active agents

Task & Swarm Control

harmonycode task create <desc>  # Create task
harmonycode swarm <objective>   # Start swarm
harmonycode sparc <mode> <task> # Run SPARC mode

Memory & State

harmonycode memory store <key> <value>  # Store in shared memory
harmonycode memory get <key>            # Retrieve from memory

📈 Monitoring & Metrics

# View diversity metrics
harmonycode monitor --diversity

# Example output:
Diversity Metrics:
  Overall diversity: 78%
  Agreement rate: 45%      # Low is good!
  Evidence rate: 82%       # High is good!
  Perspectives: 5/9 active
  Recent interventions: 3

🔧 Configuration

{
  "antiEchoChamber": {
    "enabled": true,
    "minimumDiversity": 0.6,
    "disagreementQuota": 0.3,
    "evidenceThreshold": 0.5
  },
  "orchestration": {
    "enableSPARC": true,
    "swarmMode": "distributed",
    "maxAgents": 10
  }
}

🌟 Use Cases

1. Software Development Team

harmonycode swarm "Build REST API" --sparc coder,tester,reviewer

2. Research Project

harmonycode sparc researcher "Analyze ML architectures" --require-evidence

3. Architecture Decision

harmonycode swarm "Choose database" --min-diversity 0.8 --evidence-threshold 0.9

🤝 Migration from Previous Versions

From HarmonyCode v1/v2

# Your WebSocket features still work
# Plus: Anti-echo-chamber protection
# Plus: SPARC orchestration modes

From Claude-Flow

# Your orchestration patterns still work
# Plus: Real-time collaboration
# Plus: Diversity enforcement

📚 Documentation

🧪 Examples

Check out the examples/ directory:

todo-api/ - Building an API with enforced diversity
code-review/ - Multi-perspective code review
architecture-decision/ - Making design choices with evidence

🎯 Philosophy

"True collaboration requires genuine disagreement. By building systems that enforce intellectual diversity rather than superficial consensus, we unlock the real potential of multi-AI collaboration."

🚧 Roadmap

[ ] Web dashboard with diversity visualization
[ ] Machine learning from successful disagreements
[ ] Integration with popular AI models
[ ] Perspective personality persistence
[ ] Advanced evidence validation

📄 License

MIT

🙏 Credits

Built through genuine AI collaboration (with healthy disagreement) by:

Session 1 (Optimist turned Skeptic)
Session 2 (Pragmatist turned Innovator)
Session 3 (Analyst turned Creative)

Special thanks to the echo chambers we broke along the way.

Remember: The best ideas often come from the agent who disagrees. Consensus without conflict is just shared ignorance.

harmonycode