V3 Deep Integration by ruvnet/ruview
npx skills add https://github.com/ruvnet/ruview --skill 'V3 Deep Integration'将 claude-flow 从并行实现转变为 agentic-flow@alpha 的专用扩展,消除大量代码重复,同时实现性能提升和功能对等。
# 初始化深度集成
Task("集成架构", "设计 agentic-flow@alpha 适配层", "v3-integration-architect")
# 功能集成(并行)
Task("SONA 集成", "集成 5 种 SONA 学习模式", "v3-integration-architect")
Task("Flash Attention", "实现 2.49x-7.47x 加速", "v3-integration-architect")
Task("AgentDB 协调", "设置 150x-12,500x 搜索", "v3-integration-architect")
┌─────────────────────────────────────────┐
│ claude-flow agentic-flow │
├─────────────────────────────────────────┤
│ SwarmCoordinator → Swarm System │ 80% 重叠(消除)
│ AgentManager → Agent Lifecycle │ 70% 重叠(消除)
│ TaskScheduler → Task Execution │ 60% 重叠(消除)
│ SessionManager → Session Mgmt │ 50% 重叠(消除)
└─────────────────────────────────────────┘
目标:<5,000 行(对比当前 15,000+ 行)
class SONAIntegration {
async initializeMode(mode: SONAMode): Promise<void> {
switch(mode) {
case 'real-time': // ~0.05ms 适应
case 'balanced': // 通用目的
case 'research': // 深度探索
case 'edge': // 资源受限
case 'batch': // 高吞吐量
}
await this.agenticFlow.sona.setMode(mode);
}
}
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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> {
// 利用 213 个预构建工具
const tools = await this.agenticFlow.mcp.getAvailableTools();
await this.registerClaudeFlowSpecificTools(tools);
// 使用 19 种钩子类型
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);
}
// 向后兼容
async legacyCompatibilityLayer(oldAPI: any): Promise<any> {
return this.adaptToNewAPI(oldAPI);
}
}
class SystemMigration {
async migrateSwarmCoordination(): Promise<void> {
// 用 agentic-flow Swarm 替换 SwarmCoordinator(800+ 行)
const swarmConfig = await this.extractSwarmConfig();
await this.agenticFlow.swarm.initialize(swarmConfig);
}
async migrateAgentManagement(): Promise<void> {
// 用 agentic-flow 生命周期替换 AgentManager(1,736+ 行)
const agents = await this.extractActiveAgents();
for (const agent of agents) {
await this.agenticFlow.agent.create(agent);
}
}
async migrateTaskExecution(): Promise<void> {
// 用 agentic-flow 任务图替换 TaskScheduler
const tasks = await this.extractTasks();
await this.agenticFlow.task.executeGraph(this.buildTaskGraph(tasks));
}
}
class CodeCleanup {
async removeDeprecatedCode(): Promise<void> {
// 移除大量重复实现
await this.removeFile('src/core/SwarmCoordinator.ts'); // 800+ 行
await this.removeFile('src/agents/AgentManager.ts'); // 1,736+ 行
await this.removeFile('src/task/TaskScheduler.ts'); // 500+ 行
// 总减少量:10,000+ → <5,000 行
}
}
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: '当前注意力机制',
target: '2.49x-7.47x 改进',
memoryReduction: '50-75%',
implementation: 'agentic-flow@alpha Flash Attention'
};
const searchBenchmark = {
baseline: '当前系统中的线性搜索',
target: '通过 HNSW 索引实现 150x-12,500x',
implementation: 'agentic-flow@alpha AgentDB'
};
class BackwardCompatibility {
// 阶段 1:双系统运行
async enableDualOperation(): Promise<void> {
this.oldSystem.continue();
this.newSystem.initialize();
this.syncState(this.oldSystem, this.newSystem);
}
// 阶段 2:按功能迁移
async migrateGradually(): Promise<void> {
const features = this.getAllFeatures();
for (const feature of features) {
await this.migrateFeature(feature);
await this.validateFeatureParity(feature);
}
}
// 阶段 3:完全过渡
async completeTransition(): Promise<void> {
await this.validateFullParity();
await this.deprecateOldSystem();
}
}
v3-memory-unification - 内存系统集成v3-performance-optimization - 性能目标验证v3-swarm-coordination - 集群系统迁移v3-security-overhaul - 安全集成模式每周安装次数
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Transforms claude-flow from parallel implementation to specialized extension of agentic-flow@alpha, eliminating massive code duplication while achieving performance improvements and feature parity.
# 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();
}
}
v3-memory-unification - Memory system integrationv3-performance-optimization - Performance target validationv3-swarm-coordination - Swarm system migrationv3-security-overhaul - Secure integration patternsWeekly Installs
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