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Hive Mind Intelligence
rUv edited this page Jul 24, 2025
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Hive-Mind Intelligence is Claude-Flow's revolutionary AI coordination system that orchestrates multiple specialized agents to work together on complex development tasks. Inspired by natural hive systems, it features a Queen-led architecture with specialized worker agents that coordinate through shared memory and neural pattern recognition.
👑 Queen Agent (Coordinator)
│
┌────┼────┬────┬────┐
│ │ │ │ │
🏗️ 💻 🧪 📊 🔍
Arch. Code Test Anal. Rsrch.
- 👑 Queen Agent: Central coordinator that orchestrates tasks and manages resources
- 🏗️ Architect: Designs system architecture and component relationships
- 💻 Coder: Implements features, fixes bugs, and writes code
- 🧪 Tester: Creates tests, validates functionality, and ensures quality
- 📊 Analyst: Analyzes performance, patterns, and optimization opportunities
- 🔍 Researcher: Gathers information, explores solutions, and provides context
graph TD
Q[Queen Agent] --> M[Shared Memory]
A[Architect] --> M
C[Coder] --> M
T[Tester] --> M
M --> N[Neural Patterns]
N --> Q
# Initialize with default settings
claude-flow hive init
# Specify topology and agents
claude-flow hive init --topology mesh --agents 5
# Advanced configuration
claude-flow hive init \
--topology hierarchical \
--agents 8 \
--memory-size 1GB \
--neural-patterns enabledAgent1 ←→ Agent2
↕ ↕
Agent4 ←→ Agent3
- Best for: Collaborative tasks, brainstorming, parallel problem-solving
- Performance: High coordination, moderate efficiency
- Use cases: Full-stack development, complex integrations
Queen
╱ │ ╲
A1 A2 A3
╱│╲
A4 A5 A6
- Best for: Large projects, clear task delegation, structured workflows
- Performance: High efficiency, structured coordination
- Use cases: Enterprise applications, microservices architecture
Agent1 → Agent2 → Agent3
↑ ↓
Agent5 ← Agent4 ←──╯
- Best for: Sequential workflows, pipeline processing
- Performance: Moderate coordination, high consistency
- Use cases: CI/CD pipelines, data processing workflows
Queen
╱│╲│╱
A1 A2 A3
A4 A5
- Best for: Centralized control, simple coordination
- Performance: High control, moderate scalability
- Use cases: Simple projects, prototyping, learning
The hive-mind learns from successful interactions and optimizes future coordination:
# Enable neural learning
claude-flow neural enable --pattern coordination
# Train on successful workflows
claude-flow neural train \
--pattern_type coordination \
--training_data "successful API development workflows"
# View learned patterns
claude-flow neural patterns list --type coordinationClaude-Flow includes 27+ cognitive models:
- Coordination Patterns: How agents best work together
- Problem-Solving Strategies: Optimal approaches for different task types
- Code Quality Patterns: Best practices learned from successful implementations
- Testing Strategies: Effective test generation and validation approaches
- Architecture Decisions: Proven architectural patterns for different scales
The SQLite memory system includes 12 specialized tables:
-- Core coordination tables
swarm_state -- Current hive status and configuration
agent_interactions -- Inter-agent communication logs
task_history -- Completed tasks and outcomes
decision_tree -- Decision-making patterns and rationale
-- Performance and learning tables
performance_metrics -- Execution time, success rates, efficiency
neural_patterns -- Learned coordination patterns
code_patterns -- Successful code implementations
error_patterns -- Common mistakes and their solutions
-- Project context tables
project_context -- Current project state and requirements
file_changes -- Tracked file modifications and reasons
dependencies -- Project dependencies and relationships
documentation -- Generated docs and explanations# Store coordination decision
claude-flow memory store \
"coordination/task-123" \
"Assigned API development to coder-1, testing to tester-1"
# Retrieve coordination history
claude-flow memory recall "coordination/*" --limit 10
# Search for patterns
claude-flow memory search "authentication" --context project
# Export project memory
claude-flow memory export --project current --format sqliteclaude-flow orchestrate "build user authentication" --parallel- All agents work simultaneously on different aspects
- Fastest execution for independent tasks
- Requires good task decomposition
claude-flow orchestrate "deploy to production" --sequential- Agents work in predefined order
- Better for dependent tasks
- More predictable, but slower
claude-flow orchestrate "optimize database performance" --adaptive- Automatically switches between parallel/sequential based on task dependencies
- Uses neural patterns to determine optimal approach
- Best for complex, multi-faceted problems
claude-flow orchestrate "full-stack application" --hybrid- Combines multiple coordination strategies
- Parallel for independent components, sequential for dependencies
- Optimal for large, complex projects
# Enable auto-scaling based on workload
claude-flow hive config set auto-scale true
claude-flow hive config set min-agents 2
claude-flow hive config set max-agents 12
# Scale based on task complexity
claude-flow orchestrate "complex microservices app" --auto-scale# Spawn specific agent types
claude-flow agent spawn architect --capabilities "system-design,microservices"
claude-flow agent spawn coder --capabilities "react,node.js,typescript"
claude-flow agent spawn tester --capabilities "jest,cypress,load-testing"
claude-flow agent spawn analyst --capabilities "performance,security,metrics"
claude-flow agent spawn researcher --capabilities "libraries,patterns,best-practices"# Monitor hive activity
claude-flow hive monitor --live --interval 2s
# View agent communications
claude-flow hive comms --tail --agent all
# Performance dashboard
claude-flow hive dashboard --web --port 8080# Generate performance report
claude-flow hive report --timeframe 24h --format detailed
# Analyze coordination efficiency
claude-flow hive analyze --metric coordination-efficiency
# View success rates by agent type
claude-flow hive stats --by-agent --metric success-rate# .claude-flow/agents.yml
agents:
custom-architect:
type: architect
capabilities:
- microservices
- event-sourcing
- domain-driven-design
neural_patterns:
- enterprise-architecture
- scalability-patterns
memory_access: read-write
coordination_priority: high
custom-security:
type: specialist
capabilities:
- security-analysis
- penetration-testing
- compliance-review
neural_patterns:
- security-patterns
- vulnerability-detection
memory_access: read-only
coordination_priority: critical# .claude-flow/coordination.yml
policies:
task_assignment:
strategy: capability-based
load_balancing: enabled
max_concurrent_tasks: 3
communication:
frequency: real-time
conflict_resolution: queen-decides
consensus_threshold: 0.7
learning:
pattern_recognition: enabled
feedback_loop: immediate
adaptation_rate: moderate# Enable fault tolerance
claude-flow hive config set fault-tolerance enabled
# Configure recovery strategies
claude-flow hive config set recovery-strategy "restart-failed-agents"
claude-flow hive config set max-retries 3
claude-flow hive config set timeout 300s# Check hive health
claude-flow hive health --comprehensive
# Monitor individual agents
claude-flow agent health --agent all --continuous
# Automated recovery
claude-flow hive recovery --auto --strategy conservative- Mesh: For collaborative, exploratory tasks
- Hierarchical: For large, structured projects
- Ring: For sequential, pipeline-based workflows
- Star: For simple, centralized coordination
- 2-3 agents: Simple tasks, prototyping
- 4-6 agents: Medium complexity projects
- 7-12 agents: Large, complex applications
- 12+ agents: Enterprise-scale development
- Store important decisions and rationale
- Regular memory exports for backup
- Clean up old patterns periodically
- Use namespaces for project organization
- Enable learning for repeated task types
- Review and curate learned patterns
- Export successful patterns for reuse
- Regular pattern validation and updates
# Evolve hive based on performance
claude-flow hive evolve --generations 5 --fitness coordination-speed
# Genetic algorithm optimization
claude-flow hive optimize --algorithm genetic --target efficiency# Create multiple specialized hives
claude-flow hive create frontend --topology mesh --agents 4
claude-flow hive create backend --topology hierarchical --agents 6
# Coordinate between hives
claude-flow hive coordinate --hives frontend,backend --task "full-stack app"Next Steps:
- Explore Agent Types for detailed agent capabilities
- Learn about Memory System for advanced memory usage
- Try Workflow Orchestration for complex task management