hive-mind-advanced

Hive Mind Advanced Skill

Master the advanced Hive Mind collective intelligence system for sophisticated multi-agent coordination using queen-led architecture, Byzantine consensus, and collective memory.

Overview

The Hive Mind system represents the pinnacle of multi-agent coordination in Claude Flow, implementing a queen-led hierarchical architecture where a strategic queen coordinator directs specialized worker agents through collective decision-making and shared memory.

Core Concepts

Architecture Patterns

Queen-Led Coordination

• Strategic queen agents orchestrate high-level objectives
• Tactical queens manage mid-level execution
• Adaptive queens dynamically adjust strategies based on performance

Worker Specialization

• Researcher agents: Analysis and investigation
• Coder agents: Implementation and development
• Analyst agents: Data processing and metrics
• Tester agents: Quality assurance and validation
• Architect agents: System design and planning
• Reviewer agents: Code review and improvement
• Optimizer agents: Performance enhancement
• Documenter agents: Documentation generation

Collective Memory System

• Shared knowledge base across all agents
• LRU cache with memory pressure handling
• SQLite persistence with WAL mode
• Memory consolidation and association
• Access pattern tracking and optimization

Consensus Mechanisms

Majority Consensus Simple voting where the option with most votes wins.

Weighted Consensus Queen vote counts as 3x weight, providing strategic guidance.

Byzantine Fault Tolerance Requires 2/3 majority for decision approval, ensuring robust consensus even with faulty agents.

Getting Started

1. Initialize Hive Mind

# Basic initialization
npx claude-flow hive-mind init

# Force reinitialize
npx claude-flow hive-mind init --force

# Custom configuration
npx claude-flow hive-mind init --config hive-config.json

2. Spawn a Swarm

# Basic spawn with objective
npx claude-flow hive-mind spawn "Build microservices architecture"

# Strategic queen type
npx claude-flow hive-mind spawn "Research AI patterns" --queen-type strategic

# Tactical queen with max workers
npx claude-flow hive-mind spawn "Implement API" --queen-type tactical --max-workers 12

# Adaptive queen with consensus
npx claude-flow hive-mind spawn "Optimize system" --queen-type adaptive --consensus byzantine

# Generate Claude Code commands
npx claude-flow hive-mind spawn "Build full-stack app" --claude

3. Monitor Status

# Check hive mind status
npx claude-flow hive-mind status

# Get detailed metrics
npx claude-flow hive-mind metrics

# Monitor collective memory
npx claude-flow hive-mind memory

Advanced Workflows

Session Management

Create and Manage Sessions

# List active sessions
npx claude-flow hive-mind sessions

# Pause a session
npx claude-flow hive-mind pause <session-id>

# Resume a paused session
npx claude-flow hive-mind resume <session-id>

# Stop a running session
npx claude-flow hive-mind stop <session-id>

Session Features

• Automatic checkpoint creation
• Progress tracking with completion percentages
• Parent-child process management
• Session logs with event tracking
• Export/import capabilities

Consensus Building

The Hive Mind builds consensus through structured voting:

// Programmatic consensus building
const decision = await hiveMind.buildConsensus(
  'Architecture pattern selection',
  ['microservices', 'monolith', 'serverless']
);

// Result includes:
// - decision: Winning option
// - confidence: Vote percentage
// - votes: Individual agent votes

Consensus Algorithms

1. Majority - Simple democratic voting
2. Weighted - Queen has 3x voting power
3. Byzantine - 2/3 supermajority required

Collective Memory

Storing Knowledge

// Store in collective memory
await memory.store('api-patterns', {
  rest: { pros: [...], cons: [...] },
  graphql: { pros: [...], cons: [...] }
}, 'knowledge', { confidence: 0.95 });

Memory Types

• knowledge: Permanent insights (no TTL)
• context: Session context (1 hour TTL)
• task: Task-specific data (30 min TTL)
• result: Execution results (permanent, compressed)
• error: Error logs (24 hour TTL)
• metric: Performance metrics (1 hour TTL)
• consensus: Decision records (permanent)
• system: System configuration (permanent)

Searching and Retrieval

// Search memory by pattern
const results = await memory.search('api*', {
  type: 'knowledge',
  minConfidence: 0.8,
  limit: 50
});

// Get related memories
const related = await memory.getRelated('api-patterns', 10);

// Build associations
await memory.associate('rest-api', 'authentication', 0.9);

Task Distribution

Automatic Worker Assignment

The system intelligently assigns tasks based on:

• Keyword matching with agent specialization
• Historical performance metrics
• Worker availability and load
• Task complexity analysis

// Create task (auto-assigned)
const task = await hiveMind.createTask(
  'Implement user authentication',
  priority: 8,
  { estimatedDuration: 30000 }
);

Auto-Scaling

// Configure auto-scaling
const config = {
  autoScale: true,
  maxWorkers: 12,
  scaleUpThreshold: 2, // Pending tasks per idle worker
  scaleDownThreshold: 2 // Idle workers above pending tasks
};

Integration Patterns

With Claude Code

Generate Claude Code spawn commands directly:

npx claude-flow hive-mind spawn "Build REST API" --claude

Output:

Task("Queen Coordinator", "Orchestrate REST API development...", "coordinator")
Task("Backend Developer", "Implement Express routes...", "backend-dev")
Task("Database Architect", "Design PostgreSQL schema...", "code-analyzer")
Task("Test Engineer", "Create Jest test suite...", "tester")

With SPARC Methodology

# Use hive mind for SPARC workflow
npx claude-flow sparc tdd "User authentication" --hive-mind

# Spawns:
# - Specification agent
# - Architecture agent
# - Coder agents
# - Tester agents
# - Reviewer agents

With GitHub Integration

# Repository analysis with hive mind
npx claude-flow hive-mind spawn "Analyze repo quality" --objective "owner/repo"

# PR review coordination
npx claude-flow hive-mind spawn "Review PR #123" --queen-type tactical

Performance Optimization

Memory Optimization

The collective memory system includes advanced optimizations:

LRU Cache

• Configurable cache size (default: 1000 entries)
• Memory pressure handling (default: 50MB)
• Automatic eviction of least-used entries

Database Optimization

• WAL (Write-Ahead Logging) mode
• 64MB cache size
• 256MB memory mapping
• Prepared statements for common queries
• Automatic ANALYZE and OPTIMIZE

Object Pooling

• Query result pooling
• Memory entry pooling
• Reduced garbage collection pressure

Performance Metrics

// Get performance insights
const insights = hiveMind.getPerformanceInsights();

// Includes:
// - asyncQueue utilization
// - Batch processing stats
// - Success rates
// - Average processing times
// - Memory efficiency

Task Execution

Parallel Processing

• Batch agent spawning (5 agents per batch)
• Concurrent task orchestration
• Async operation optimization
• Non-blocking task assignment

Benchmarks

• 10-20x faster batch spawning
• 2.8-4.4x speed improvement overall
• 32.3% token reduction
• 84.8% SWE-Bench solve rate

Configuration

Hive Mind Config

{
  "objective": "Build microservices",
  "name": "my-hive",
  "queenType": "strategic", // strategic | tactical | adaptive
  "maxWorkers": 8,
  "consensusAlgorithm": "byzantine", // majority | weighted | byzantine
  "autoScale": true,
  "memorySize": 100, // MB
  "taskTimeout": 60, // minutes
  "encryption": false
}

Memory Config

{
  "maxSize": 100, // MB
  "compressionThreshold": 1024, // bytes
  "gcInterval": 300000, // 5 minutes
  "cacheSize": 1000,
  "cacheMemoryMB": 50,
  "enablePooling": true,
  "enableAsyncOperations": true
}

Hooks Integration

Hive Mind integrates with Claude Flow hooks for automation:

Pre-Task Hooks

• Auto-assign agents by file type
• Validate objective complexity
• Optimize topology selection
• Cache search patterns

Post-Task Hooks

• Auto-format deliverables
• Train neural patterns
• Update collective memory
• Analyze performance bottlenecks

Session Hooks

• Generate session summaries
• Persist checkpoint data
• Track comprehensive metrics
• Restore execution context

Best Practices

1. Choose the Right Queen Type

Strategic Queens - For research, planning, and analysis

npx claude-flow hive-mind spawn "Research ML frameworks" --queen-type strategic

Tactical Queens - For implementation and execution

npx claude-flow hive-mind spawn "Build authentication" --queen-type tactical

Adaptive Queens - For optimization and dynamic tasks

npx claude-flow hive-mind spawn "Optimize performance" --queen-type adaptive

2. Leverage Consensus

Use consensus for critical decisions:

• Architecture pattern selection
• Technology stack choices
• Implementation approach
• Code review approval
• Release readiness

3. Utilize Collective Memory

Store Learnings

// After successful pattern implementation
await memory.store('auth-pattern', {
  approach: 'JWT with refresh tokens',
  pros: ['Stateless', 'Scalable'],
  cons: ['Token size', 'Revocation complexity'],
  implementation: {...}
}, 'knowledge', { confidence: 0.95 });

Build Associations

// Link related concepts
await memory.associate('jwt-auth', 'refresh-tokens', 0.9);
await memory.associate('jwt-auth', 'oauth2', 0.7);

4. Monitor Performance

# Regular status checks
npx claude-flow hive-mind status

# Track me

---

*Content truncated.*