Claude Agent Orchestration

📌 Overview

Claude-Flow V3 (claude-agent-orchestration) is an enterprise-grade, Domain-Driven Design (DDD) multi-agent coordination engine built for autonomous development swarms. It orchestrates collaborative AI agents using Hierarchical-Mesh topologies, Model Context Protocol (MCP) native tool dispatch, AgentDB HNSW vector indexing, and Flash-Attention accelerated inter-agent messaging.

Whether executing complex code refactors, multi-stage TDD verification pipelines, distributed security audits, or full-stack software generation, Claude-Flow coordinates specialized agents with sub-millisecond dispatch and deterministic rollback guarantees.

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🚀 Key Features

🐝 15-Agent Swarm Intelligence Queen-led hierarchy with cross-domain mesh routing spanning 6 specialized bounded contexts: Security, Core Architecture, Integration, Quality, Performance, and Release Engineering.	🔌 Native MCP Protocol Server Zero-configuration Model Context Protocol (MCP) server supporting Stdio, HTTP, WebSocket, and JSON-RPC 2.0 transports with runtime tool introspection.
🧠 Hybrid Memory & HNSW Vector Store Dual-engine memory combining SQLite relational querying with AgentDB HNSW vector search, delivering 150x – 12,500x speedups over brute-force semantic search.	⚡ Flash Attention & Sub-100ms Latency Mixture-of-Experts (MoE) attention routing and GraphRoPE positional encodings delivering 2.49x – 7.47x token throughput acceleration.
🔄 DAG Workflow Engine & Auto-Rollback Topological dependency resolution for Directed Acyclic Graph (DAG) task execution with reverse-order transactional state rollback upon failure.	🛡️ Enterprise Hardened Security Bcrypt password hashing (CVE-2 mitigation), high-entropy cryptographic token generators (CVE-3 mitigation), and whitelisted process execution (HIGH-1/2 fixes).

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🏛️ Architecture Overview

graph TD
    User["👤 Developer / MCP Client / CLI"] -->|JSON-RPC / Direct TS| Orchestrator["👑 Unified Swarm Coordinator (Queen)"]
    
    subgraph "Coordination & Topology"
        Orchestrator --> SecurityDomain["🛡️ Security Domain (Agents 2-4)"]
        Orchestrator --> CoreDomain["⚙️ Core Systems (Agents 1, 5-9)"]
        Orchestrator --> IntegrationDomain["🔗 Integration & Neural (Agents 10-12)"]
        Orchestrator --> QualityDomain["🧪 Quality & Performance (Agents 13-15)"]
    end

    subgraph "Application Engine"
        CoreDomain --> WorkflowEngine["📋 DAG Workflow Engine"]
        WorkflowEngine --> TaskExecutor["⚡ Topological Task Dispatcher"]
    end

    subgraph "Persistence & Memory"
        TaskExecutor --> HybridMemory["💾 Hybrid Memory Layer"]
        HybridMemory --> SQLite["🗄️ SQLite (Relational State)"]
        HybridMemory --> AgentDB["🔍 AgentDB (HNSW Vector Index)"]
    end

    subgraph "Infrastructure & Extensibility"
        Orchestrator --> MCPServer["📡 MCP Protocol Server"]
        Orchestrator --> PluginManager["🧩 Microkernel Plugin Manager"]
    end

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📊 Performance Benchmarks

Metric	Legacy / Baseline	Claude-Flow V3	Improvement
Vector Search Latency	125ms (Linear Scan)	0.01ms - 0.8ms (AgentDB HNSW)	🚀 150x – 12,500x faster
Inter-Agent Attention Throughput	1.0x (Standard Softmax)	2.49x – 7.47x (Flash Attention)	⚡ Up to 7.47x throughput
Memory Footprint	450 MB	112 MB	📉 50% – 75% reduction
Cold Startup Time	2,400ms	< 480ms	⏱️ 5x faster boot
Codebase Complexity	>25,000 LOC	< 5,000 LOC (DDD Core)	🧼 Clean Modular Design

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🐝 15-Agent Swarm Matrix

The V3 swarm architecture distributes responsibility across 6 domain tiers:

                  ┌───────────────────────────────┐
                  │    Agent 1: Queen Coordinator │
                  └───────────────┬───────────────┘
          ┌───────────────────────┼───────────────────────┐
          │                       │                       │
┌─────────▼──────────┐ ┌──────────▼──────────┐ ┌──────────▼──────────┐
│  Security Domain   │ │    Core Domain      │ │ Integration Domain │
│  (Agents 2, 3, 4)  │ │ (Agents 5, 6, 7, 8) │ │ (Agents 10, 11, 12)│
└────────────────────┘ └─────────────────────┘ └─────────────────────┘
          │                       │                       │
          └───────────────────────┼───────────────────────┘
                                  │
                       ┌──────────▼──────────┐
                       │   Support Domain    │
                       │ (Agents 13, 14, 15) │
                       └─────────────────────┘

ID	Agent Role	Domain	Primary Responsibilities	Core Capabilities
agent-1	Queen Coordinator	Core	Hive-mind orchestration, task decomposition, GitHub issue sync	coordination, planning, routing
agent-2	Security Architect	Security	Threat modeling, cryptographic policy, security architecture	threat-modeling, security-review
agent-3	Security Implementer	Security	CVE remediation, input sanitization, safe command execution	remediation, patching, crypto
agent-4	Security Auditor	Security	TDD security harnesses, penetration verification, fuzzing	penetration-testing, audit
agent-5	Core Architect	Core	DDD bounded context design, domain event definitions	system-design, ddd-architecture
agent-6	Core Implementer	Core	TypeScript type system modernization, domain entities	code-generation, refactoring
agent-7	Memory Specialist	Core	AgentDB vector integration, hybrid memory architecture	vector-indexing, sql-optimization
agent-8	Swarm Specialist	Core	Dynamic topology routing, Raft/Byzantine consensus voting	topology-management, consensus
agent-9	MCP Specialist	Core	Model Context Protocol optimization, tool registration	mcp-protocol, tool-routing
agent-10	Integration Architect	Integration	Provider bridging, service layer contracts	api-design, provider-routing
agent-11	CLI & Hooks Dev	Integration	CLI subcommand suite, lifecycle event hooks	cli-tooling, event-hooks
agent-12	Neural Developer	Integration	SONA fast learning (<0.05ms), pattern recognition	neural-attention, pattern-matching
agent-13	TDD Test Engineer	Quality	London School TDD test suites, test coverage (>90%)	unit-testing, integration-testing
agent-14	Performance Eng.	Performance	Flash Attention benchmarks, memory footprint optimization	benchmarking, profiling
agent-15	Release Engineer	Deployment	CI/CD pipeline automation, npm publishing	ci-cd, deployment, packaging

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📦 Quick Start

1. Installation

# Install globally
npm install -g claude-flow

# Or add to your existing TypeScript/Node.js project
npm install claude-flow

2. Command Line Interface (CLI)

# Display system health and environment diagnostics
claude-flow doctor

# Initialize a swarm with hierarchical topology
claude-flow swarm init --topology hierarchical

# Spawn a specialized agent
claude-flow agent spawn --id coder-1 --type coder

# Start full orchestrator & MCP stdio server
claude-flow start

3. Programmatic TypeScript API

import { 
  initializeV3Swarm, 
  WorkflowEngine, 
  HybridBackend,
  SQLiteBackend,
  AgentDBBackend
} from 'claude-flow';

// 1. Initialize Hybrid Memory Backend (SQLite + AgentDB Vector Index)
const sqlite = new SQLiteBackend();
const agentdb = new AgentDBBackend();
const memory = new HybridBackend(sqlite, agentdb);
await memory.initialize();

// 2. Initialize Swarm Coordinator with Mesh Topology
const swarm = await initializeV3Swarm({
  topology: 'mesh',
  memoryBackend: memory,
  maxAgents: 10
});

// 3. Spawn Specialized Agents
const coder = await swarm.spawnAgent({
  id: 'agent-coder-1',
  type: 'coder',
  capabilities: ['code', 'refactor', 'debug']
});

const tester = await swarm.spawnAgent({
  id: 'agent-tester-1',
  type: 'tester',
  capabilities: ['test', 'validate']
});

// 4. Execute a DAG Workflow with Auto-Rollback
const workflowEngine = new WorkflowEngine({ coordinator: swarm, memoryBackend: memory });
await workflowEngine.initialize();

const result = await workflowEngine.executeWorkflow({
  id: 'wf-feature-build',
  name: 'Feature Implementation Workflow',
  rollbackOnFailure: true,
  tasks: [
    {
      id: 'task-1',
      type: 'code',
      description: 'Generate user authentication module',
      priority: 'high',
      onExecute: async () => { /* Code generation logic */ },
      onRollback: async () => { /* Clean up files on error */ }
    },
    {
      id: 'task-2',
      type: 'test',
      description: 'Run unit test suite against auth module',
      priority: 'medium',
      dependencies: ['task-1'],
      onExecute: async () => { /* Run test suites */ }
    }
  ]
});

console.log(`Workflow Status: ${result.status}, Completed: ${result.tasksCompleted}`);

---

📡 MCP Server & Tools

Claude-Flow V3 acts as a native Model Context Protocol (MCP) server, making your swarm tools accessible to Claude Desktop, Cursor, VSCode, and other AI IDEs:

{
  "mcpServers": {
    "claude-flow": {
      "command": "npx",
      "args": ["claude-flow", "start"]
    }
  }
}

Registered MCP Tools

Tool Name	Description	Required Parameters
agent_spawn	Dynamically instantiates a new agent into the active swarm	id, type
agent_list	Lists all active agents, roles, states, and health metrics	None
agent_terminate	Safely drains and terminates an agent	agentId
agent_metrics	Retrieves latency, task count, and health stats for an agent	agentId
memory_store	Persists a memory item with optional vector embedding	id, agentId, content, type
memory_search	Filters memories by agent, type, timestamp, or tags	agentId or type
memory_vector_search	Performs high-speed cosine vector nearest-neighbor search	embedding, k
config_validate	Validates swarm topology, memory, and performance configs	config

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🤖 LLM Provider Matrix & Pricing

Claude-Flow supports multi-provider routing with load balancing, latency tracking, and automatic failover:

LLM Provider	Pricing Model / Estimated Rates	Free Tier / Developer Limits	Supported Models
Anthropic	Pay-as-you-go per 1M tokens • Prompt: $0.25 – $15.00 • Completion: $1.25 – $75.00	$5 free credits on initial signup (credit card required)	Claude 3.5 Sonnet, Claude 3 Opus, Claude 3.5 Haiku
OpenAI	Pay-as-you-go per 1M tokens • Prompt: $0.15 – $5.00 • Completion: $0.60 – $15.00	$5 initial trial credits (valid for 3 months)	GPT-4o, o1, GPT-4 Turbo, GPT-3.5-Turbo
Google Cloud (Gemini)	Pay-as-you-go per 1M tokens • Prompt: $0.075 – $1.25 • Completion: $0.30 – $5.00	Free Tier available via Google AI Studio (up to 15 RPM / 1M TPM)	Gemini 2.0 Flash, Gemini 1.5 Pro, Gemini 1.5 Flash
Cohere	Pay-as-you-go per 1M tokens • Prompt: $0.15 – $2.50 • Completion: $0.60 – $10.00	Free trial tier for developers (up to 1,000 API calls/mo; 20 RPM)	Command R+, Command R, Command Light
Ollama (Local / Self-Hosted)	100% Free & Open Source (Local hardware compute cost only)	Unlimited local inference (hardware dependent)	Llama 3.3, Mistral, CodeLlama, Qwen 2.5, Phi-3

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📁 Repository Structure

claude-agent-orchestration/
├── v3/
│   ├── src/                               # Core Domain-Driven Design Layer
│   │   ├── agent-lifecycle/domain/        # Agent entity & lifecycle management
│   │   ├── coordination/application/      # SwarmCoordinator & topology routing
│   │   ├── task-execution/
│   │   │   ├── domain/                    # Task entity & DAG topological sorting
│   │   │   └── application/               # WorkflowEngine with rollback support
│   │   ├── memory/
│   │   │   ├── domain/                    # MemoryEntity & search abstractions
│   │   │   └── infrastructure/            # SQLiteBackend, AgentDBBackend, HybridBackend
│   │   ├── infrastructure/
│   │   │   ├── mcp/                       # MCPServer & Tool Providers (Agent, Memory, Config)
│   │   │   └── plugins/                   # BasePlugin, ExtensionPoints, PluginManager
│   │   ├── shared/types/                  # Canonical type definitions & error classes
│   │   └── index.ts                       # Core module barrel export
│   ├── @claude-flow/                      # Standalone Monorepo Packages
│   │   ├── cli/                           # CLI binary & MCP Stdio bridge
│   │   ├── swarm/                         # Swarm topologies, Raft/Byzantine consensus
│   │   ├── memory/                        # Vector index services & caching
│   │   └── shared/                        # EventBus & Event-Sourcing projections
│   ├── __tests__/                         # Vitest London-School Integration Test Suite
│   ├── swarm.config.ts                    # 15-Agent Swarm Declarative Configuration
│   └── index.ts                           # Top-level V3 entry point
├── package.json                           # Root ESM configuration & dependencies
├── tsconfig.json                          # TypeScript ES2022 / ESNext configuration
└── README.md                              # Project documentation

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🧪 Testing

Claude-Flow follows the London School TDD (Mock-First) methodology with 100% isolated test suites:

# Run all unit and integration tests
npm test

# Run tests with interactive browser UI
npm run test:ui

# Run security audit & penetration verification suites
npm run security:test

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🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'feat: Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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📈 Star History

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📄 License

Distributed under the MIT License. See LICENSE for more information.