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name	hive-mind-advanced
description	Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory
version	1.0.0
category	coordination
tags	["hive-mind","swarm","queen-worker","consensus","collective-intelligence","multi-agent","coordination"]
author	Claude Flow Team
hooks	{"pre":"echo \"🧠 Hive Mind Advanced activated\"\nif [ -d \"/workspaces/ruvector/.claude/intelligence\" ]; then\n cd /workspaces/ruvector/.claude/intelligence\n INTELLIGENCE_MODE=treatment node cli.js pre-edit \"$FILE\" 2>/dev/null \|\| true\nfi\n","post":"echo \"✅ Hive Mind Advanced complete\"\nif [ -d \"/workspaces/ruvector/.claude/intelligence\" ]; then\n cd /workspaces/ruvector/.claude/intelligence\n INTELLIGENCE_MODE=treatment node cli.js post-edit \"$FILE\" \"true\" 2>/dev/null \|\| true\nfi\n"}

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.

🧠 Self-Learning Intelligence

Integrates with RuVector's Q-learning and vector memory for improved performance. CLI: node .claude/intelligence/cli.js stats

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

Majority - Simple democratic voting
Weighted - Queen has 3x voting power
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 metrics
npx claude-flow hive-mind metrics

# Analyze memory usage
npx claude-flow hive-mind memory

5. Session Management

Checkpoint Frequently

// Create checkpoints at key milestones
await sessionManager.saveCheckpoint(
  sessionId,
  'api-routes-complete',
  { completedRoutes: [...], remaining: [...] }
);

Resume Sessions

# Resume from any previous state
npx claude-flow hive-mind resume <session-id>

Troubleshooting

Memory Issues

High Memory Usage

# Run garbage collection
npx claude-flow hive-mind memory --gc

# Optimize database
npx claude-flow hive-mind memory --optimize

# Export and clear
npx claude-flow hive-mind memory --export --clear

Low Cache Hit Rate

// Increase cache size in config
{
  "cacheSize": 2000,
  "cacheMemoryMB": 100
}

Performance Issues

Slow Task Assignment

// Enable worker type caching
// The system caches best worker matches for 5 minutes
// Automatic - no configuration needed

High Queue Utilization

// Increase async queue concurrency
{
  "asyncQueueConcurrency": 20 // Default: min(maxWorkers * 2, 20)
}

Consensus Failures

No Consensus Reached (Byzantine)

# Switch to weighted consensus for more decisive results
npx claude-flow hive-mind spawn "..." --consensus weighted

# Or use simple majority
npx claude-flow hive-mind spawn "..." --consensus majority

Advanced Topics

Custom Worker Types

Define specialized workers in .claude/agents/:

name: security-auditor
type: specialist
capabilities:
  - vulnerability-scanning
  - security-review
  - penetration-testing
  - compliance-checking
priority: high

Neural Pattern Training

The system trains on successful patterns:

// Automatic pattern learning
// Happens after successful task completion
// Stores in collective memory
// Improves future task matching

Multi-Hive Coordination

Run multiple hive minds simultaneously:

# Frontend hive
npx claude-flow hive-mind spawn "Build UI" --name frontend-hive

# Backend hive
npx claude-flow hive-mind spawn "Build API" --name backend-hive

# They share collective memory for coordination

Export/Import Sessions

# Export session for backup
npx claude-flow hive-mind export <session-id> --output backup.json

# Import session
npx claude-flow hive-mind import backup.json

API Reference

HiveMindCore

const hiveMind = new HiveMindCore({
  objective: 'Build system',
  queenType: 'strategic',
  maxWorkers: 8,
  consensusAlgorithm: 'byzantine'
});

await hiveMind.initialize();
await hiveMind.spawnQueen(queenData);
await hiveMind.spawnWorkers(['coder', 'tester']);
await hiveMind.createTask('Implement feature', 7);
const decision = await hiveMind.buildConsensus('topic', options);
const status = hiveMind.getStatus();
await hiveMind.shutdown();

CollectiveMemory

const memory = new CollectiveMemory({
  swarmId: 'hive-123',
  maxSize: 100,
  cacheSize: 1000
});

await memory.store(key, value, type, metadata);
const data = await memory.retrieve(key);
const results = await memory.search(pattern, options);
const related = await memory.getRelated(key, limit);
await memory.associate(key1, key2, strength);
const stats = memory.getStatistics();
const analytics = memory.getAnalytics();
const health = await memory.healthCheck();

HiveMindSessionManager

const sessionManager = new HiveMindSessionManager();

const sessionId = await sessionManager.createSession(
  swarmId, swarmName, objective, metadata
);

await sessionManager.saveCheckpoint(sessionId, name, data);
const sessions = await sessionManager.getActiveSessions();
const session = await sessionManager.getSession(sessionId);
await sessionManager.pauseSession(sessionId);
await sessionManager.resumeSession(sessionId);
await sessionManager.stopSession(sessionId);
await sessionManager.completeSession(sessionId);

Examples

Full-Stack Development

# Initialize hive mind
npx claude-flow hive-mind init

# Spawn full-stack hive
npx claude-flow hive-mind spawn "Build e-commerce platform" \
  --queen-type strategic \
  --max-workers 10 \
  --consensus weighted \
  --claude

# Output generates Claude Code commands:
# - Queen coordinator
# - Frontend developers (React)
# - Backend developers (Node.js)
# - Database architects
# - DevOps engineers
# - Security auditors
# - Test engineers
# - Documentation specialists

Research and Analysis

# Spawn research hive
npx claude-flow hive-mind spawn "Research GraphQL vs REST" \
  --queen-type adaptive \
  --consensus byzantine

# Researchers gather data
# Analysts process findings
# Queen builds consensus on recommendation
# Results stored in collective memory

Code Review

# Review coordination
npx claude-flow hive-mind spawn "Review PR #456" \
  --queen-type tactical \
  --max-workers 6

# Spawns:
# - Code analyzers
# - Security reviewers
# - Performance reviewers
# - Test coverage analyzers
# - Documentation reviewers
# - Consensus on approval/changes

Skill Progression

Beginner

Initialize hive mind
Spawn basic swarms
Monitor status
Use majority consensus

Intermediate

Configure queen types
Implement session management
Use weighted consensus
Access collective memory
Enable auto-scaling

Advanced

Byzantine fault tolerance
Memory optimization
Custom worker types
Multi-hive coordination
Neural pattern training
Session export/import
Performance tuning

Related Skills

swarm-orchestration: Basic swarm coordination
consensus-mechanisms: Distributed decision making
memory-systems: Advanced memory management
sparc-methodology: Structured development workflow
github-integration: Repository coordination

References

Skill Version: 1.0.0 Last Updated: 2025-10-19 Maintained By: Claude Flow Team License: MIT