Claude-code-flow V3 Deep Integration
Deep agentic-flow@alpha integration implementing ADR-001. Eliminates 10,000+ duplicate lines by building claude-flow as specialized extension rather than parallel implementation.
install
source · Clone the upstream repo
git clone https://github.com/ruvnet/ruflo
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/ruvnet/ruflo "$T" && mkdir -p ~/.claude/skills && cp -r "$T/.claude/skills/v3-integration-deep" ~/.claude/skills/ruvnet-claude-code-flow-v3-deep-integration-4e835f && rm -rf "$T"
manifest:
.claude/skills/v3-integration-deep/SKILL.mdsource content
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
- Memory system integrationv3-memory-unification
- Performance target validationv3-performance-optimization
- Swarm system migrationv3-swarm-coordination
- Secure integration patternsv3-security-overhaul