v3/plugins/neural-coordination/README.md
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.
npm install @claude-flow/plugin-neural-coordination
npx claude-flow plugins install --name @claude-flow/plugin-neural-coordination
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);
coordination/neural-consensusAchieve 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.
coordination/topology-optimizeOptimize 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.
coordination/collective-memoryManage 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.
coordination/emergent-protocolDevelop 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.
coordination/swarm-behaviorOrchestrate 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.
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')[];
}
| 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 |
| 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 |
| 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 |
| 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 |
sona - Self-Optimizing Neural Architecture for agent adaptationruvector-gnn-wasm - Communication graph optimization and message routingruvector-nervous-system-wasm - Neural coordination layer for collective behaviorruvector-attention-wasm - Multi-head attention for agent-to-agent communicationruvector-learning-wasm - Multi-agent reinforcement learning (MARL)| 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 |
MIT