flow-nexus-swarm

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Flow Nexus Swarm & Workflow Orchestration

Flow Nexus Swarm与工作流编排

Deploy and manage cloud-based AI agent swarms with event-driven workflow automation, message queue processing, and intelligent agent coordination.

部署并管理基于云端的AI Agent集群，实现事件驱动工作流自动化、消息队列处理与智能Agent协同。

📋 Table of Contents

概述
Swarm管理
工作流自动化
Agent编排
模板与模式
高级功能
最佳实践

Overview

概述

Flow Nexus provides cloud-based orchestration for AI agent swarms with:

Multi-topology Support: Hierarchical, mesh, ring, and star architectures
Event-driven Workflows: Message queue processing with async execution
Template Library: Pre-built swarm configurations for common use cases
Intelligent Agent Assignment: Vector similarity matching for optimal agent selection
Real-time Monitoring: Comprehensive metrics and audit trails
Scalable Infrastructure: Cloud-based execution with auto-scaling

Flow Nexus 为AI Agent集群提供云端编排能力，具备以下特性：

多拓扑支持：分层、网状、环形和星形架构
事件驱动工作流：基于消息队列的异步执行处理
模板库：针对常见场景预构建的Swarm配置
智能Agent分配：通过向量相似度匹配选择最优Agent
实时监控：全面的指标统计与审计追踪
可扩展基础设施：支持自动扩缩容的云端执行环境

Swarm Management

Swarm管理

Initialize Swarm

初始化Swarm

Create a new swarm with specified topology and configuration:

javascript

mcp__flow-nexus__swarm_init({
  topology: "hierarchical", // Options: mesh, ring, star, hierarchical
  maxAgents: 8,
  strategy: "balanced" // Options: balanced, specialized, adaptive
})

Topology Guide:

Hierarchical: Tree structure with coordinator nodes (best for complex projects)
Mesh: Peer-to-peer collaboration (best for research and analysis)
Ring: Circular coordination (best for sequential workflows)
Star: Centralized hub (best for simple delegation)

Strategy Guide:

Balanced: Equal distribution of workload across agents
Specialized: Agents focus on specific expertise areas
Adaptive: Dynamic adjustment based on task complexity

创建指定拓扑结构与配置的新Swarm：

javascript

mcp__flow-nexus__swarm_init({
  topology: "hierarchical", // 可选：mesh, ring, star, hierarchical
  maxAgents: 8,
  strategy: "balanced" // 可选：balanced, specialized, adaptive
})

拓扑指南：

分层架构：带协调节点的树形结构（适用于复杂项目）
网状架构：点对点协作模式（适用于研究与分析场景）
环形架构：循环协调模式（适用于顺序工作流）
星形架构：中心化枢纽模式（适用于简单任务分配）

策略指南：

均衡策略：在Agent间平均分配工作负载
专精策略：Agent专注于特定专业领域
自适应策略：根据任务复杂度动态调整

Spawn Agents

生成Agent

Add specialized agents to the swarm:

javascript

mcp__flow-nexus__agent_spawn({
  type: "researcher", // Options: researcher, coder, analyst, optimizer, coordinator
  name: "Lead Researcher",
  capabilities: ["web_search", "analysis", "summarization"]
})

Agent Types:

Researcher: Information gathering, web search, analysis
Coder: Code generation, refactoring, implementation
Analyst: Data analysis, pattern recognition, insights
Optimizer: Performance tuning, resource optimization
Coordinator: Task delegation, progress tracking, integration

向Swarm中添加专精型Agent：

javascript

mcp__flow-nexus__agent_spawn({
  type: "researcher", // 可选：researcher, coder, analyst, optimizer, coordinator
  name: "Lead Researcher",
  capabilities: ["web_search", "analysis", "summarization"]
})

Agent类型：

Researcher（研究员）：信息收集、网络搜索、分析
Coder（编码者）：代码生成、重构、实现
Analyst（分析师）：数据分析、模式识别、洞察提炼
Optimizer（优化者）：性能调优、资源优化
Coordinator（协调者）：任务分配、进度追踪、集成管理

Orchestrate Tasks

任务编排

Distribute tasks across the swarm:

javascript

mcp__flow-nexus__task_orchestrate({
  task: "Build a REST API with authentication and database integration",
  strategy: "parallel", // Options: parallel, sequential, adaptive
  maxAgents: 5,
  priority: "high" // Options: low, medium, high, critical
})

Execution Strategies:

Parallel: Maximum concurrency for independent subtasks
Sequential: Step-by-step execution with dependencies
Adaptive: AI-powered strategy selection based on task analysis

在Swarm中分配任务：

javascript

mcp__flow-nexus__task_orchestrate({
  task: "Build a REST API with authentication and database integration",
  strategy: "parallel", // 可选：parallel, sequential, adaptive
  maxAgents: 5,
  priority: "high" // 可选：low, medium, high, critical
})

执行策略：

并行执行：独立子任务最大并发处理
顺序执行：带依赖关系的分步执行
自适应执行：基于任务分析的AI驱动策略选择

Monitor & Scale Swarms

监控与扩缩容Swarm

javascript

// Get detailed swarm status
mcp__flow-nexus__swarm_status({
  swarm_id: "optional-id" // Uses active swarm if not provided
})

// List all active swarms
mcp__flow-nexus__swarm_list({
  status: "active" // Options: active, destroyed, all
})

// Scale swarm up or down
mcp__flow-nexus__swarm_scale({
  target_agents: 10,
  swarm_id: "optional-id"
})

// Gracefully destroy swarm
mcp__flow-nexus__swarm_destroy({
  swarm_id: "optional-id"
})

javascript

// 获取详细的Swarm状态
mcp__flow-nexus__swarm_status({
  swarm_id: "optional-id" // 未提供则使用当前活跃Swarm
})

// 列出所有活跃Swarm
mcp__flow-nexus__swarm_list({
  status: "active" // 可选：active, destroyed, all
})

// 向上或向下扩缩容Swarm
mcp__flow-nexus__swarm_scale({
  target_agents: 10,
  swarm_id: "optional-id"
})

// 优雅销毁Swarm
mcp__flow-nexus__swarm_destroy({
  swarm_id: "optional-id"
})

Workflow Automation

工作流自动化

Create Workflow

创建工作流

Define event-driven workflows with message queue processing:

javascript

mcp__flow-nexus__workflow_create({
  name: "CI/CD Pipeline",
  description: "Automated testing, building, and deployment",
  steps: [
    {
      id: "test",
      action: "run_tests",
      agent: "tester",
      parallel: true
    },
    {
      id: "build",
      action: "build_app",
      agent: "builder",
      depends_on: ["test"]
    },
    {
      id: "deploy",
      action: "deploy_prod",
      agent: "deployer",
      depends_on: ["build"]
    }
  ],
  triggers: ["push_to_main", "manual_trigger"],
  metadata: {
    priority: 10,
    retry_policy: "exponential_backoff"
  }
})

Workflow Features:

Dependency Management: Define step dependencies with
```
depends_on
```
Parallel Execution: Set
```
parallel: true
```
for concurrent steps
Event Triggers: GitHub events, schedules, manual triggers
Retry Policies: Automatic retry on transient failures
Priority Queuing: High-priority workflows execute first

定义基于消息队列处理的事件驱动工作流：

javascript

mcp__flow-nexus__workflow_create({
  name: "CI/CD Pipeline",
  description: "Automated testing, building, and deployment",
  steps: [
    {
      id: "test",
      action: "run_tests",
      agent: "tester",
      parallel: true
    },
    {
      id: "build",
      action: "build_app",
      agent: "builder",
      depends_on: ["test"]
    },
    {
      id: "deploy",
      action: "deploy_prod",
      agent: "deployer",
      depends_on: ["build"]
    }
  ],
  triggers: ["push_to_main", "manual_trigger"],
  metadata: {
    priority: 10,
    retry_policy: "exponential_backoff"
  }
})

工作流特性：

依赖管理：通过
```
depends_on
```
定义步骤依赖关系
并行执行：设置
```
parallel: true
```
实现步骤并发处理
事件触发器：GitHub事件、定时任务、手动触发
重试策略：针对临时故障自动重试
优先级队列：高优先级工作流优先执行

Execute Workflow

执行工作流

Run workflows synchronously or asynchronously:

javascript

mcp__flow-nexus__workflow_execute({
  workflow_id: "workflow_id",
  input_data: {
    branch: "main",
    commit: "abc123",
    environment: "production"
  },
  async: true // Queue-based execution for long-running workflows
})

Execution Modes:

Sync (async: false): Immediate execution, wait for completion
Async (async: true): Message queue processing, non-blocking

同步或异步运行工作流：

javascript

mcp__flow-nexus__workflow_execute({
  workflow_id: "workflow_id",
  input_data: {
    branch: "main",
    commit: "abc123",
    environment: "production"
  },
  async: true // 针对长时工作流使用队列执行
})

执行模式：

同步（async: false）：立即执行，等待完成
异步（async: true）：基于消息队列的非阻塞执行

Monitor Workflows

监控工作流

javascript

// Get workflow status and metrics
mcp__flow-nexus__workflow_status({
  workflow_id: "id",
  execution_id: "specific-run-id", // Optional
  include_metrics: true
})

// List workflows with filters
mcp__flow-nexus__workflow_list({
  status: "running", // Options: running, completed, failed, pending
  limit: 10,
  offset: 0
})

// Get complete audit trail
mcp__flow-nexus__workflow_audit_trail({
  workflow_id: "id",
  limit: 50,
  start_time: "2025-01-01T00:00:00Z"
})

javascript

// 获取工作流状态与指标
mcp__flow-nexus__workflow_status({
  workflow_id: "id",
  execution_id: "specific-run-id", // 可选
  include_metrics: true
})

// 带筛选条件列出工作流
mcp__flow-nexus__workflow_list({
  status: "running", // 可选：running, completed, failed, pending
  limit: 10,
  offset: 0
})

// 获取完整审计追踪
mcp__flow-nexus__workflow_audit_trail({
  workflow_id: "id",
  limit: 50,
  start_time: "2025-01-01T00:00:00Z"
})

Agent Assignment

Agent分配

Intelligently assign agents to workflow tasks:

javascript

mcp__flow-nexus__workflow_agent_assign({
  task_id: "task_id",
  agent_type: "coder", // Preferred agent type
  use_vector_similarity: true // AI-powered capability matching
})

Vector Similarity Matching:

Analyzes task requirements and agent capabilities
Finds optimal agent based on past performance
Considers workload and availability

为工作流任务智能分配Agent：

javascript

mcp__flow-nexus__workflow_agent_assign({
  task_id: "task_id",
  agent_type: "coder", // 首选Agent类型
  use_vector_similarity: true // 基于AI的能力匹配
})

向量相似度匹配：

分析任务需求与Agent能力
根据过往表现选择最优Agent
考虑工作负载与可用性

Queue Management

队列管理

Monitor and manage message queues:

javascript

mcp__flow-nexus__workflow_queue_status({
  queue_name: "optional-specific-queue",
  include_messages: true // Show pending messages
})

监控与管理消息队列：

javascript

mcp__flow-nexus__workflow_queue_status({
  queue_name: "optional-specific-queue",
  include_messages: true // 显示待处理消息
})

Agent Orchestration

Agent编排

Full-Stack Development Pattern

全栈开发模式

javascript

// 1. Initialize swarm with hierarchical topology
mcp__flow-nexus__swarm_init({
  topology: "hierarchical",
  maxAgents: 8,
  strategy: "specialized"
})

// 2. Spawn specialized agents
mcp__flow-nexus__agent_spawn({ type: "coordinator", name: "Project Manager" })
mcp__flow-nexus__agent_spawn({ type: "coder", name: "Backend Developer" })
mcp__flow-nexus__agent_spawn({ type: "coder", name: "Frontend Developer" })
mcp__flow-nexus__agent_spawn({ type: "coder", name: "Database Architect" })
mcp__flow-nexus__agent_spawn({ type: "analyst", name: "QA Engineer" })

// 3. Create development workflow
mcp__flow-nexus__workflow_create({
  name: "Full-Stack Development",
  steps: [
    { id: "requirements", action: "analyze_requirements", agent: "coordinator" },
    { id: "db_design", action: "design_schema", agent: "Database Architect" },
    { id: "backend", action: "build_api", agent: "Backend Developer", depends_on: ["db_design"] },
    { id: "frontend", action: "build_ui", agent: "Frontend Developer", depends_on: ["requirements"] },
    { id: "integration", action: "integrate", agent: "Backend Developer", depends_on: ["backend", "frontend"] },
    { id: "testing", action: "qa_testing", agent: "QA Engineer", depends_on: ["integration"] }
  ]
})

// 4. Execute workflow
mcp__flow-nexus__workflow_execute({
  workflow_id: "workflow_id",
  input_data: {
    project: "E-commerce Platform",
    tech_stack: ["Node.js", "React", "PostgreSQL"]
  }
})

javascript

// 1. 初始化分层拓扑的Swarm
mcp__flow-nexus__swarm_init({
  topology: "hierarchical",
  maxAgents: 8,
  strategy: "specialized"
})

// 2. 生成专精型Agent
mcp__flow-nexus__agent_spawn({ type: "coordinator", name: "Project Manager" })
mcp__flow-nexus__agent_spawn({ type: "coder", name: "Backend Developer" })
mcp__flow-nexus__agent_spawn({ type: "coder", name: "Frontend Developer" })
mcp__flow-nexus__agent_spawn({ type: "coder", name: "Database Architect" })
mcp__flow-nexus__agent_spawn({ type: "analyst", name: "QA Engineer" })

// 3. 创建开发工作流
mcp__flow-nexus__workflow_create({
  name: "Full-Stack Development",
  steps: [
    { id: "requirements", action: "analyze_requirements", agent: "coordinator" },
    { id: "db_design", action: "design_schema", agent: "Database Architect" },
    { id: "backend", action: "build_api", agent: "Backend Developer", depends_on: ["db_design"] },
    { id: "frontend", action: "build_ui", agent: "Frontend Developer", depends_on: ["requirements"] },
    { id: "integration", action: "integrate", agent: "Backend Developer", depends_on: ["backend", "frontend"] },
    { id: "testing", action: "qa_testing", agent: "QA Engineer", depends_on: ["integration"] }
  ]
})

// 4. 执行工作流
mcp__flow-nexus__workflow_execute({
  workflow_id: "workflow_id",
  input_data: {
    project: "E-commerce Platform",
    tech_stack: ["Node.js", "React", "PostgreSQL"]
  }
})

Research & Analysis Pattern

研究与分析模式

javascript

// 1. Initialize mesh topology for collaborative research
mcp__flow-nexus__swarm_init({
  topology: "mesh",
  maxAgents: 5,
  strategy: "balanced"
})

// 2. Spawn research agents
mcp__flow-nexus__agent_spawn({ type: "researcher", name: "Primary Researcher" })
mcp__flow-nexus__agent_spawn({ type: "researcher", name: "Secondary Researcher" })
mcp__flow-nexus__agent_spawn({ type: "analyst", name: "Data Analyst" })
mcp__flow-nexus__agent_spawn({ type: "analyst", name: "Insights Analyst" })

// 3. Orchestrate research task
mcp__flow-nexus__task_orchestrate({
  task: "Research machine learning trends for 2025 and analyze market opportunities",
  strategy: "parallel",
  maxAgents: 4,
  priority: "high"
})

javascript

// 1. 初始化网状拓扑的Swarm用于协作研究
mcp__flow-nexus__swarm_init({
  topology: "mesh",
  maxAgents: 5,
  strategy: "balanced"
})

// 2. 生成研究型Agent
mcp__flow-nexus__agent_spawn({ type: "researcher", name: "Primary Researcher" })
mcp__flow-nexus__agent_spawn({ type: "researcher", name: "Secondary Researcher" })
mcp__flow-nexus__agent_spawn({ type: "analyst", name: "Data Analyst" })
mcp__flow-nexus__agent_spawn({ type: "analyst", name: "Insights Analyst" })

// 3. 编排研究任务
mcp__flow-nexus__task_orchestrate({
  task: "Research machine learning trends for 2025 and analyze market opportunities",
  strategy: "parallel",
  maxAgents: 4,
  priority: "high"
})

CI/CD Pipeline Pattern

CI/CD流水线模式

javascript

mcp__flow-nexus__workflow_create({
  name: "Deployment Pipeline",
  description: "Automated testing, building, and multi-environment deployment",
  steps: [
    { id: "lint", action: "lint_code", agent: "code_quality", parallel: true },
    { id: "unit_test", action: "unit_tests", agent: "test_runner", parallel: true },
    { id: "integration_test", action: "integration_tests", agent: "test_runner", parallel: true },
    { id: "build", action: "build_artifacts", agent: "builder", depends_on: ["lint", "unit_test", "integration_test"] },
    { id: "security_scan", action: "security_scan", agent: "security", depends_on: ["build"] },
    { id: "deploy_staging", action: "deploy", agent: "deployer", depends_on: ["security_scan"] },
    { id: "smoke_test", action: "smoke_tests", agent: "test_runner", depends_on: ["deploy_staging"] },
    { id: "deploy_prod", action: "deploy", agent: "deployer", depends_on: ["smoke_test"] }
  ],
  triggers: ["github_push", "github_pr_merged"],
  metadata: {
    priority: 10,
    auto_rollback: true
  }
})

javascript

mcp__flow-nexus__workflow_create({
  name: "Deployment Pipeline",
  description: "Automated testing, building, and multi-environment deployment",
  steps: [
    { id: "lint", action: "lint_code", agent: "code_quality", parallel: true },
    { id: "unit_test", action: "unit_tests", agent: "test_runner", parallel: true },
    { id: "integration_test", action: "integration_tests", agent: "test_runner", parallel: true },
    { id: "build", action: "build_artifacts", agent: "builder", depends_on: ["lint", "unit_test", "integration_test"] },
    { id: "security_scan", action: "security_scan", agent: "security", depends_on: ["build"] },
    { id: "deploy_staging", action: "deploy", agent: "deployer", depends_on: ["security_scan"] },
    { id: "smoke_test", action: "smoke_tests", agent: "test_runner", depends_on: ["deploy_staging"] },
    { id: "deploy_prod", action: "deploy", agent: "deployer", depends_on: ["smoke_test"] }
  ],
  triggers: ["github_push", "github_pr_merged"],
  metadata: {
    priority: 10,
    auto_rollback: true
  }
})

Data Processing Pipeline Pattern

数据处理流水线模式

javascript

mcp__flow-nexus__workflow_create({
  name: "ETL Pipeline",
  description: "Extract, Transform, Load data processing",
  steps: [
    { id: "extract", action: "extract_data", agent: "data_extractor" },
    { id: "validate_raw", action: "validate_data", agent: "validator", depends_on: ["extract"] },
    { id: "transform", action: "transform_data", agent: "transformer", depends_on: ["validate_raw"] },
    { id: "enrich", action: "enrich_data", agent: "enricher", depends_on: ["transform"] },
    { id: "load", action: "load_data", agent: "loader", depends_on: ["enrich"] },
    { id: "validate_final", action: "validate_data", agent: "validator", depends_on: ["load"] }
  ],
  triggers: ["schedule:0 2 * * *"], // Daily at 2 AM
  metadata: {
    retry_policy: "exponential_backoff",
    max_retries: 3
  }
})

javascript

mcp__flow-nexus__workflow_create({
  name: "ETL Pipeline",
  description: "Extract, Transform, Load data processing",
  steps: [
    { id: "extract", action: "extract_data", agent: "data_extractor" },
    { id: "validate_raw", action: "validate_data", agent: "validator", depends_on: ["extract"] },
    { id: "transform", action: "transform_data", agent: "transformer", depends_on: ["validate_raw"] },
    { id: "enrich", action: "enrich_data", agent: "enricher", depends_on: ["transform"] },
    { id: "load", action: "load_data", agent: "loader", depends_on: ["enrich"] },
    { id: "validate_final", action: "validate_data", agent: "validator", depends_on: ["load"] }
  ],
  triggers: ["schedule:0 2 * * *"], // 每日凌晨2点
  metadata: {
    retry_policy: "exponential_backoff",
    max_retries: 3
  }
})

Templates & Patterns

模板与模式

Use Pre-built Templates

使用预构建模板

javascript

// Create swarm from template
mcp__flow-nexus__swarm_create_from_template({
  template_name: "full-stack-dev",
  overrides: {
    maxAgents: 6,
    strategy: "specialized"
  }
})

// List available templates
mcp__flow-nexus__swarm_templates_list({
  category: "quickstart", // Options: quickstart, specialized, enterprise, custom, all
  includeStore: true
})

Available Template Categories:

Quickstart Templates:

```
full-stack-dev
```
: Complete web development swarm
```
research-team
```
: Research and analysis swarm
```
code-review
```
: Automated code review swarm
```
data-pipeline
```
: ETL and data processing

Specialized Templates:

```
ml-development
```
: Machine learning project swarm
```
mobile-dev
```
: Mobile app development
```
devops-automation
```
: Infrastructure and deployment
```
security-audit
```
: Security analysis and testing

Enterprise Templates:

```
enterprise-migration
```
: Large-scale system migration
```
multi-repo-sync
```
: Multi-repository coordination
```
compliance-review
```
: Regulatory compliance workflows
```
incident-response
```
: Automated incident management

javascript

// 从模板创建Swarm
mcp__flow-nexus__swarm_create_from_template({
  template_name: "full-stack-dev",
  overrides: {
    maxAgents: 6,
    strategy: "specialized"
  }
})

// 列出可用模板
mcp__flow-nexus__swarm_templates_list({
  category: "quickstart", // 可选：quickstart, specialized, enterprise, custom, all
  includeStore: true
})

可用模板分类：

快速入门模板：

```
full-stack-dev
```
：完整Web开发Swarm
```
research-team
```
：研究与分析Swarm
```
code-review
```
：自动化代码评审Swarm
```
data-pipeline
```
：ETL与数据处理Swarm

专精模板：

```
ml-development
```
：机器学习项目Swarm
```
mobile-dev
```
：移动应用开发Swarm
```
devops-automation
```
：基础设施与部署Swarm
```
security-audit
```
：安全分析与测试Swarm

企业级模板：

```
enterprise-migration
```
：大规模系统迁移Swarm
```
multi-repo-sync
```
：多仓库协调Swarm
```
compliance-review
```
：合规性审查工作流
```
incident-response
```
：自动化事件管理Swarm

Custom Template Creation

自定义模板创建

Save successful swarm configurations as reusable templates for future projects.

将成功的Swarm配置保存为可复用模板，用于未来项目。

Advanced Features

高级功能

Real-time Monitoring

实时监控

javascript

// Subscribe to execution streams
mcp__flow-nexus__execution_stream_subscribe({
  stream_type: "claude-flow-swarm",
  deployment_id: "deployment_id"
})

// Get execution status
mcp__flow-nexus__execution_stream_status({
  stream_id: "stream_id"
})

// List files created during execution
mcp__flow-nexus__execution_files_list({
  stream_id: "stream_id",
  created_by: "claude-flow"
})

javascript

// 订阅执行流
mcp__flow-nexus__execution_stream_subscribe({
  stream_type: "claude-flow-swarm",
  deployment_id: "deployment_id"
})

// 获取执行流状态
mcp__flow-nexus__execution_stream_status({
  stream_id: "stream_id"
})

// 列出执行过程中创建的文件
mcp__flow-nexus__execution_files_list({
  stream_id: "stream_id",
  created_by: "claude-flow"
})

Swarm Metrics & Analytics

Swarm指标与分析

javascript

// Get swarm performance metrics
mcp__flow-nexus__swarm_status({
  swarm_id: "id"
})

// Analyze workflow efficiency
mcp__flow-nexus__workflow_status({
  workflow_id: "id",
  include_metrics: true
})

javascript

// 获取Swarm性能指标
mcp__flow-nexus__swarm_status({
  swarm_id: "id"
})

// 分析工作流效率
mcp__flow-nexus__workflow_status({
  workflow_id: "id",
  include_metrics: true
})

Multi-Swarm Coordination

多Swarm协调

Coordinate multiple swarms for complex, multi-phase projects:

javascript

// Phase 1: Research swarm
const researchSwarm = await mcp__flow-nexus__swarm_init({
  topology: "mesh",
  maxAgents: 4
})

// Phase 2: Development swarm
const devSwarm = await mcp__flow-nexus__swarm_init({
  topology: "hierarchical",
  maxAgents: 8
})

// Phase 3: Testing swarm
const testSwarm = await mcp__flow-nexus__swarm_init({
  topology: "star",
  maxAgents: 5
})

为复杂的多阶段项目协调多个Swarm：

javascript

// 阶段1：研究Swarm
const researchSwarm = await mcp__flow-nexus__swarm_init({
  topology: "mesh",
  maxAgents: 4
})

// 阶段2：开发Swarm
const devSwarm = await mcp__flow-nexus__swarm_init({
  topology: "hierarchical",
  maxAgents: 8
})

// 阶段3：测试Swarm
const testSwarm = await mcp__flow-nexus__swarm_init({
  topology: "star",
  maxAgents: 5
})

Best Practices

最佳实践

1. Choose the Right Topology

1. 选择合适的拓扑结构

javascript

// Simple projects: Star
mcp__flow-nexus__swarm_init({ topology: "star", maxAgents: 3 })

// Collaborative work: Mesh
mcp__flow-nexus__swarm_init({ topology: "mesh", maxAgents: 5 })

// Complex projects: Hierarchical
mcp__flow-nexus__swarm_init({ topology: "hierarchical", maxAgents: 10 })

// Sequential workflows: Ring
mcp__flow-nexus__swarm_init({ topology: "ring", maxAgents: 4 })

javascript

// 简单项目：星形拓扑
mcp__flow-nexus__swarm_init({ topology: "star", maxAgents: 3 })

// 协作工作：网状拓扑
mcp__flow-nexus__swarm_init({ topology: "mesh", maxAgents: 5 })

// 复杂项目：分层拓扑
mcp__flow-nexus__swarm_init({ topology: "hierarchical", maxAgents: 10 })

// 顺序工作流：环形拓扑
mcp__flow-nexus__swarm_init({ topology: "ring", maxAgents: 4 })

2. Optimize Agent Assignment

2. 优化Agent分配

javascript

// Use vector similarity for optimal matching
mcp__flow-nexus__workflow_agent_assign({
  task_id: "complex-task",
  use_vector_similarity: true
})

javascript

// 使用向量相似度实现最优匹配
mcp__flow-nexus__workflow_agent_assign({
  task_id: "complex-task",
  use_vector_similarity: true
})

3. Implement Proper Error Handling

3. 实现完善的错误处理

javascript

mcp__flow-nexus__workflow_create({
  name: "Resilient Workflow",
  steps: [...],
  metadata: {
    retry_policy: "exponential_backoff",
    max_retries: 3,
    timeout: 300000, // 5 minutes
    on_failure: "notify_and_rollback"
  }
})

javascript

mcp__flow-nexus__workflow_create({
  name: "Resilient Workflow",
  steps: [...],
  metadata: {
    retry_policy: "exponential_backoff",
    max_retries: 3,
    timeout: 300000, // 5分钟
    on_failure: "notify_and_rollback"
  }
})

4. Monitor and Scale

4. 监控与扩缩容

javascript

// Regular monitoring
const status = await mcp__flow-nexus__swarm_status()

// Scale based on workload
if (status.workload > 0.8) {
  await mcp__flow-nexus__swarm_scale({ target_agents: status.agents + 2 })
}

javascript

// 定期监控
const status = await mcp__flow-nexus__swarm_status()

// 根据工作负载进行扩缩容
if (status.workload > 0.8) {
  await mcp__flow-nexus__swarm_scale({ target_agents: status.agents + 2 })
}

5. Use Async Execution for Long-Running Workflows

5. 对长时工作流使用异步执行

javascript

// Long-running workflows should use message queues
mcp__flow-nexus__workflow_execute({
  workflow_id: "data-pipeline",
  async: true // Non-blocking execution
})

// Monitor progress
mcp__flow-nexus__workflow_queue_status({ include_messages: true })

javascript

// 长时工作流应使用消息队列
mcp__flow-nexus__workflow_execute({
  workflow_id: "data-pipeline",
  async: true // 非阻塞执行
})

// 监控进度
mcp__flow-nexus__workflow_queue_status({ include_messages: true })

6. Clean Up Resources

6. 清理资源

javascript

// Destroy swarm when complete
mcp__flow-nexus__swarm_destroy({ swarm_id: "id" })

javascript

// 完成后销毁Swarm
mcp__flow-nexus__swarm_destroy({ swarm_id: "id" })

7. Leverage Templates

7. 利用模板

javascript

// Use proven templates instead of building from scratch
mcp__flow-nexus__swarm_create_from_template({
  template_name: "code-review",
  overrides: { maxAgents: 4 }
})

javascript

// 使用经过验证的模板而非从零开始构建
mcp__flow-nexus__swarm_create_from_template({
  template_name: "code-review",
  overrides: { maxAgents: 4 }
})

Integration with Claude Flow

与Claude Flow集成

Flow Nexus swarms integrate seamlessly with Claude Flow hooks:

bash

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Flow Nexus Swarm可与Claude Flow钩子无缝集成：

bash

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Pre-task coordination setup

任务前协调设置

npx claude-flow@alpha hooks pre-task --description "Initialize swarm"

Post-task metrics export

任务后指标导出

npx claude-flow@alpha hooks post-task --task-id "swarm-execution"

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npx claude-flow@alpha hooks post-task --task-id "swarm-execution"

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Common Use Cases

常见使用场景

1. Multi-Repo Development

1. 多仓库开发

Coordinate development across multiple repositories
Synchronized testing and deployment
Cross-repo dependency management

跨多个仓库协调开发
同步测试与部署
跨仓库依赖管理

2. Research Projects

2. 研究项目

Distributed information gathering
Parallel analysis of different data sources
Collaborative synthesis and reporting

分布式信息收集
多数据源并行分析
协作式综合与报告

3. DevOps Automation

3. DevOps自动化

Infrastructure as Code deployment
Multi-environment testing
Automated rollback and recovery

基础设施即代码部署
多环境测试
自动回滚与恢复

4. Code Quality Workflows

4. 代码质量工作流

Automated code review
Security scanning
Performance benchmarking

自动化代码评审
安全扫描
性能基准测试

5. Data Processing

5. 数据处理

Large-scale ETL pipelines
Real-time data transformation
Data validation and quality checks

大规模ETL流水线
实时数据转换
数据验证与质量检查

Authentication & Setup

认证与设置

bash

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bash

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Install Flow Nexus

安装Flow Nexus

npm install -g flow-nexus@latest

Register account

注册账户

npx flow-nexus@latest register

Login

Add MCP server to Claude Code

将MCP服务器添加到Claude Code

claude mcp add flow-nexus npx flow-nexus@latest mcp start

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claude mcp add flow-nexus npx flow-nexus@latest mcp start

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Support & Resources

支持与资源

Platform: https:/$flow-nexus.ruv.io
Documentation: https:/$github.com$ruvnet$flow-nexus
Issues: https:/$github.com$ruvnet$flow-nexus$issues

Remember: Flow Nexus provides cloud-based orchestration infrastructure. For local execution and coordination, use the core

claude-flow

MCP server alongside Flow Nexus for maximum flexibility.

平台：https:/$flow-nexus.ruv.io
文档：https:/$github.com$ruvnet$flow-nexus
问题反馈：https:/$github.com$ruvnet$flow-nexus$issues

注意：Flow Nexus提供云端编排基础设施。如需本地执行与协调，请结合核心

claude-flow

MCP服务器与Flow Nexus使用，以获得最大灵活性。