ruvnet__ruflo/v3/plugins/neural-coordination

@claude-flow/plugin-neural-coordination

A cutting-edge multi-agent coordination plugin combining the SONA self-optimizing neural architecture with graph neural networks for agent communication topology optimization. The plugin enables emergent protocol development, neural consensus mechanisms, collective memory formation, and adaptive swarm behavior while maintaining interpretability of agent interactions.

Installation

npm

npm install @claude-flow/plugin-neural-coordination

CLI

npx claude-flow plugins install --name @claude-flow/plugin-neural-coordination

Quick Start

import { NeuralCoordinationPlugin } from '@claude-flow/plugin-neural-coordination';

// Initialize the plugin
const plugin = new NeuralCoordinationPlugin();
await plugin.initialize();

// Achieve consensus among agents
const consensus = await plugin.neuralConsensus({
  proposal: {
    topic: 'architecture-decision',
    options: [
      { id: 'microservices', value: { pattern: 'microservices', complexity: 'high' } },
      { id: 'monolith', value: { pattern: 'monolith', complexity: 'low' } }
    ],
    constraints: { maxLatency: 100, minReliability: 0.99 }
  },
  agents: [
    { id: 'architect', preferences: { scalability: 0.8, simplicity: 0.2 } },
    { id: 'ops', preferences: { scalability: 0.3, simplicity: 0.7 } },
    { id: 'developer', preferences: { scalability: 0.5, simplicity: 0.5 } }
  ],
  protocol: 'iterative_refinement',
  maxRounds: 10
});

console.log('Consensus reached:', consensus.decision);

Available MCP Tools

1. coordination/neural-consensus

Achieve agent consensus using neural negotiation protocols.

const result = await mcp.call('coordination/neural-consensus', {
  proposal: {
    topic: 'resource-allocation',
    options: [
      { id: 'option-a', value: { cpus: 4, memory: '8GB' } },
      { id: 'option-b', value: { cpus: 8, memory: '4GB' } }
    ],
    constraints: { budget: 100 }
  },
  agents: [
    { id: 'agent-1', preferences: { performance: 0.9 }, constraints: {} },
    { id: 'agent-2', preferences: { cost: 0.8 }, constraints: {} }
  ],
  protocol: 'neural_voting',
  maxRounds: 5
});

Returns: Consensus decision with voting breakdown, confidence scores, and round-by-round negotiation history.

2. coordination/topology-optimize

Optimize agent communication topology for efficiency using GNN analysis.

const result = await mcp.call('coordination/topology-optimize', {
  agents: [
    { id: 'coordinator', capabilities: ['planning', 'delegation'], location: { zone: 'us-east' } },
    { id: 'worker-1', capabilities: ['coding'], location: { zone: 'us-east' } },
    { id: 'worker-2', capabilities: ['testing'], location: { zone: 'us-west' } }
  ],
  objective: 'minimize_latency',
  constraints: {
    maxConnections: 10,
    minRedundancy: 2,
    preferredTopology: 'hybrid'
  }
});

Returns: Optimized communication graph with connection weights and routing recommendations.

3. coordination/collective-memory

Manage shared collective memory across agent swarms.

const result = await mcp.call('coordination/collective-memory', {
  action: 'store',
  memory: {
    key: 'project-context',
    value: { requirements: [...], decisions: [...] },
    importance: 0.9,
    expiry: '2025-12-31T23:59:59Z'
  },
  scope: 'team',
  consolidationStrategy: 'ewc'
});

Returns: Memory operation status with synchronization metadata across agents.

4. coordination/emergent-protocol

Develop emergent communication protocols through multi-agent reinforcement learning.

const result = await mcp.call('coordination/emergent-protocol', {
  task: {
    type: 'cooperative_search',
    objectives: ['find_target', 'minimize_time'],
    constraints: { maxSteps: 100 }
  },
  communicationBudget: {
    symbolsPerMessage: 10,
    messagesPerRound: 3
  },
  trainingEpisodes: 1000,
  interpretability: true
});

Returns: Learned communication protocol with symbol vocabulary and usage patterns.

5. coordination/swarm-behavior

Orchestrate emergent swarm behaviors using neural coordination.

const result = await mcp.call('coordination/swarm-behavior', {
  behavior: 'task_allocation',
  parameters: {
    taskQueue: [...],
    priorityWeights: { urgency: 0.7, complexity: 0.3 }
  },
  adaptiveRules: true,
  observability: {
    recordTrajectories: true,
    measureEmergence: true
  }
});

Returns: Swarm behavior execution plan with agent assignments and adaptation metrics.

Configuration Options

interface NeuralCoordinationConfig {
  // Maximum number of agents in coordination (default: 1000)
  maxAgents: number;

  // Memory limit per agent (default: 1GB)
  memoryLimitPerAgent: number;

  // Consensus timeout per round in ms (default: 60000)
  consensusTimeoutMs: number;

  // Enable Byzantine fault tolerance (default: true)
  enableBFT: boolean;

  // Message signing for security (default: true)
  signMessages: boolean;

  // Supported consensus protocols
  protocols: ('neural_voting' | 'iterative_refinement' | 'auction' | 'contract_net')[];
}

Performance Targets

Metric	Target	Improvement vs Baseline
Consensus convergence (100 agents)	<100 rounds	10x faster
Communication overhead	<10% of total compute	3x reduction
Topology optimization (1000 nodes)	<1s	60x faster
Memory synchronization	<100ms eventual consistency	10x faster
Emergent protocol training	<1 hour for basic tasks	Novel capability

Security Considerations

• Agent Authentication: Every agent must be authenticated with signed credentials before joining coordination
• Message Signing: All inter-agent messages are cryptographically signed (Ed25519) to prevent spoofing
• Byzantine Fault Tolerance: Consensus tolerates up to f < n/3 malicious/faulty agents
• Sybil Attack Prevention: Agent credential verification and rate limiting prevent fake agent multiplication
• Memory Encryption: Collective memory is encrypted at rest (AES-256-GCM) with session-specific keys
• Input Validation: All inputs validated with Zod schemas to prevent injection attacks

WASM Security Constraints

Constraint	Value	Rationale
Memory Limit per Agent	1GB max	Prevent resource exhaustion
CPU Time per Round	60 seconds	Prevent consensus deadlock
No External Network	Enforced	Isolated agent communication only
Signed Messages	Ed25519 required	Prevent message tampering
Session Isolation	Per-coordination	Prevent cross-session leakage

Rate Limits

Tool	Requests/Minute	Max Concurrent
neural-consensus	10	2
topology-optimize	5	1
collective-memory	100	10
emergent-protocol	1	1
swarm-behavior	10	2

Input Limits

Constraint	Limit
Max agents per coordination	1,000
Max message size	1MB
Max rounds per consensus	1,000
Memory limit per agent	1GB
CPU time per round	60 seconds

Dependencies

• sona - Self-Optimizing Neural Architecture for agent adaptation
• ruvector-gnn-wasm - Communication graph optimization and message routing
• ruvector-nervous-system-wasm - Neural coordination layer for collective behavior
• ruvector-attention-wasm - Multi-head attention for agent-to-agent communication
• ruvector-learning-wasm - Multi-agent reinforcement learning (MARL)

Use Cases

1. Distributed Problem Solving: Coordinate agents to solve complex decomposed problems
2. Negotiation Systems: Multi-party negotiation with optimal outcomes
3. Swarm Robotics: Emergent collective behaviors for physical agents
4. Federated Learning: Coordinate model training across distributed agents
5. Market Simulation: Agent-based modeling with realistic interactions

Related Plugins

Plugin	Description	Synergy
@claude-flow/plugin-cognitive-kernel	Cognitive augmentation with working memory	Enhances individual agent reasoning within coordinated swarms
@claude-flow/plugin-quantum-optimizer	Quantum-inspired optimization	Optimizes task allocation and resource scheduling across agents
@claude-flow/plugin-hyperbolic-reasoning	Hierarchical reasoning	Enables hierarchical agent organization and taxonomic coordination

License

MIT