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V3 Deep Integration

Migrates Codex-flow onto agentic-flow@alpha adapters to eliminate duplicate orchestration code.

Updated May 11, 2026
Search Tags:#typescript#migration#agentdb#agentic-flow#adapter pattern#code deduplication#flash attention
One-click install
npx skills add https://github.com/Turgunoff/mebellar_app --skill v3-deep-integration-turgunoff
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: V3 Deep Integration
Source: https://github.com/Turgunoff/mebellar_app/tree/main/.agents/skills/v3-integration-deep
Command: npx skills add https://github.com/Turgunoff/mebellar_app --skill v3-deep-integration-turgunoff

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires agentic-flow.

What problem does it solve? Codex-flow duplicates thousands of lines of orchestration logic (swarm coordination, agent lifecycle, task scheduling) that already exist in agentic-flow@alpha, creating maintenance burden and missing upstream performance gains. ## Core Features & Use Cases - Adapter Layer Migration: Extends agentic-flow@alpha Agent classes with backward-compatible wrappers, replacing SwarmCoordinator, AgentManager, and TaskScheduler implementations. - Feature Integration: Wires in SONA learning modes, Flash Attention (2.49x-7.47x speedup), AgentDB HNSW search (150x-12,500x), and 213 MCP tools with 19 hook types. - Phased Rollout: Supports dual-operation, feature-by-feature migration with parity validation, then deprecation of legacy code. - Use Case: A platform team reducing a 15,000+ line orchestration codebase to under 5,000 lines while gaining RL algorithms (PPO, DQN, MCTS) and sub-0.05ms SONA adaptation. ## Quick Start Ask the agent to design the agentic-flow@alpha adapter layer and plan the phased migration of swarm coordination, agent management, and task execution.

Frequently Asked Questions about V3 Deep Integration

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I migrate Codex-flow to agentic-flow@alpha?▼

Follow the three-phase plan: build an adapter layer extending agentic-flow Agent classes, migrate systems one by one (swarm, agents, tasks) with parity validation, then remove deprecated files like SwarmCoordinator and AgentManager.

How to integrate Flash Attention for transformer speedup?▼

Call the agentic-flow attention API with flashAttention, targeting 2.49x-7.47x speedup and 50-75% memory reduction across multi-head, linear, local, and global mechanisms.

Does the migration maintain backward compatibility with v2?▼

Yes. The plan uses dual operation where old and new systems run in parallel with state sync, then migrates feature-by-feature with parity validation before deprecating the legacy system.

What performance gains does AgentDB HNSW indexing provide?▼

AgentDB cross-agent memory with HNSW indexing targets 150x-12,500x search speedup over linear search, using 1536-dimension vectors for shared agent memory.

What are the limitations of the deep integration approach?▼

The migration depends on agentic-flow@alpha stability and requires careful parity validation per feature. Systems with heavy customization of SwarmCoordinator or AgentManager may need additional adapter logic before removal.

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