V3 Deep Integration
What This Skill Does
Transforms claude-flow from parallel implementation to specialized extension of agentic-flow@alpha, eliminating massive code duplication while achieving performance improvements and feature parity.
Quick Start
# Initialize deep integration
Task("Integration architecture", "Design agentic-flow@alpha adapter layer", "v3-integration-architect")
# Feature integration (parallel)
Task("SONA integration", "Integrate 5 SONA learning modes", "v3-integration-architect")
Task("Flash Attention", "Implement 2.49x-7.47x speedup", "v3-integration-architect")
Task("AgentDB coordination", "Setup 150x-12,500x search", "v3-integration-architect")
Code Deduplication Strategy
Current Overlap → Integration
┌─────────────────────────────────────────┐
│ claude-flow agentic-flow │
├─────────────────────────────────────────┤
│ SwarmCoordinator → Swarm System │ 80% overlap (eliminate)
│ AgentManager → Agent Lifecycle │ 70% overlap (eliminate)
│ TaskScheduler → Task Execution │ 60% overlap (eliminate)
│ SessionManager → Session Mgmt │ 50% overlap (eliminate)
└─────────────────────────────────────────┘
TARGET: <5,000 lines (vs 15,000+ currently)
agentic-flow@alpha Feature Integration
SONA Learning Modes
class SONAIntegration {
async initializeMode(mode: SONAMode): Promise<void> {
switch(mode) {
case 'real-time': // ~0.05ms adaptation
case 'balanced': // general purpose
case 'research': // deep exploration
case 'edge': // resource-constrained
case 'batch': // high-throughput
}
await this.agenticFlow.sona.setMode(mode);
}
}
Flash Attention Integration
class FlashAttentionIntegration {
async optimizeAttention(): Promise<AttentionResult> {
return this.agenticFlow.attention.flashAttention({
speedupTarget: '2.49x-7.47x',
memoryReduction: '50-75%',
mechanisms: ['multi-head', 'linear', 'local', 'global']
});
}
}
AgentDB Coordination
class AgentDBIntegration {
async setupCrossAgentMemory(): Promise<void> {
await this.agentdb.enableCrossAgentSharing({
indexType: 'HNSW',
speedupTarget: '150x-12500x',
dimensions: 1536
});
}
}
MCP Tools Integration
class MCPToolsIntegration {
async integrateBuiltinTools(): Promise<void> {
// Leverage 213 pre-built tools
const tools = await this.agenticFlow.mcp.getAvailableTools();
await this.registerClaudeFlowSpecificTools(tools);
// Use 19 hook types
const hookTypes = await this.agenticFlow.hooks.getTypes();
await this.configureClaudeFlowHooks(hookTypes);
}
}
Migration Implementation
Phase 1: Adapter Layer
import { Agent as AgenticFlowAgent } from 'agentic-flow@alpha';
export class ClaudeFlowAgent extends AgenticFlowAgent {
async handleClaudeFlowTask(task: ClaudeTask): Promise<TaskResult> {
return this.executeWithSONA(task);
}
// Backward compatibility
async legacyCompatibilityLayer(oldAPI: any): Promise<any> {
return this.adaptToNewAPI(oldAPI);
}
}
Phase 2: System Migration
class SystemMigration {
async migrateSwarmCoordination(): Promise<void> {
// Replace SwarmCoordinator (800+ lines) with agentic-flow Swarm
const swarmConfig = await this.extractSwarmConfig();
await this.agenticFlow.swarm.initialize(swarmConfig);
}
async migrateAgentManagement(): Promise<void> {
// Replace AgentManager (1,736+ lines) with agentic-flow lifecycle
const agents = await this.extractActiveAgents();
for (const agent of agents) {
await this.agenticFlow.agent.create(agent);
}
}
async migrateTaskExecution(): Promise<void> {
// Replace TaskScheduler with agentic-flow task graph
const tasks = await this.extractTasks();
await this.agenticFlow.task.executeGraph(this.buildTaskGraph(tasks));
}
}
Phase 3: Cleanup
class CodeCleanup {
async removeDeprecatedCode(): Promise<void> {
// Remove massive duplicate implementations
await this.removeFile('src/core/SwarmCoordinator.ts'); // 800+ lines
await this.removeFile('src/agents/AgentManager.ts'); // 1,736+ lines
await this.removeFile('src/task/TaskScheduler.ts'); // 500+ lines
// Total reduction: 10,000+ → <5,000 lines
}
}
RL Algorithm Integration
class RLIntegration {
algorithms = [
'PPO', 'DQN', 'A2C', 'MCTS', 'Q-Learning',
'SARSA', 'Actor-Critic', 'Decision-Transformer'
];
async optimizeAgentBehavior(): Promise<void> {
for (const algorithm of this.algorithms) {
await this.agenticFlow.rl.train(algorithm, {
episodes: 1000,
rewardFunction: this.claudeFlowRewardFunction
});
}
}
}
Performance Integration
Flash Attention Targets
const attentionBenchmark = {
baseline: 'current attention mechanism',
target: '2.49x-7.47x improvement',
memoryReduction: '50-75%',
implementation: 'agentic-flow@alpha Flash Attention'
};
AgentDB Search Performance
const searchBenchmark = {
baseline: 'linear search in current systems',
target: '150x-12,500x via HNSW indexing',
implementation: 'agentic-flow@alpha AgentDB'
};
Backward Compatibility
Gradual Migration
class BackwardCompatibility {
// Phase 1: Dual operation
async enableDualOperation(): Promise<void> {
this.oldSystem.continue();
this.newSystem.initialize();
this.syncState(this.oldSystem, this.newSystem);
}
// Phase 2: Feature-by-feature migration
async migrateGradually(): Promise<void> {
const features = this.getAllFeatures();
for (const feature of features) {
await this.migrateFeature(feature);
await this.validateFeatureParity(feature);
}
}
// Phase 3: Complete transition
async completeTransition(): Promise<void> {
await this.validateFullParity();
await this.deprecateOldSystem();
}
}
Success Metrics
- Code Reduction: <5,000 lines orchestration (vs 15,000+)
- Performance: 2.49x-7.47x Flash Attention speedup
- Search: 150x-12,500x AgentDB improvement
- Memory: 50-75% usage reduction
- Feature Parity: 100% v2 functionality maintained
- SONA: <0.05ms adaptation time
- Integration: All 213 MCP tools + 19 hook types available
Related V3 Skills
v3-memory-unification - Memory system integrationv3-performance-optimization - Performance target validationv3-swarm-coordination - Swarm system migrationv3-security-overhaul - Secure integration patterns