V3 Deep Integration

# 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")

┌─────────────────────────────────────────┐
│  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)

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);
  }
}

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"],
    });
  }
}

class AgentDBIntegration {
  async setupCrossAgentMemory(): Promise<void> {
    await this.agentdb.enableCrossAgentSharing({
      indexType: "HNSW",
      speedupTarget: "150x-12500x",
      dimensions: 1536,
    });
  }
}

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);
  }
}

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);
  }
}

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));
  }
}

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
  }
}

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,
      });
    }
  }
}

const attentionBenchmark = {
  baseline: "current attention mechanism",
  target: "2.49x-7.47x improvement",
  memoryReduction: "50-75%",
  implementation: "agentic-flow@alpha Flash Attention",
};

const searchBenchmark = {
  baseline: "linear search in current systems",
  target: "150x-12,500x via HNSW indexing",
  implementation: "agentic-flow@alpha AgentDB",
};

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();
  }
}