flow-nexus-swarm

$2b

• 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

Swarm Management

Initialize Swarm

Create a new swarm with specified topology and configuration:

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

Spawn Agents

Add specialized agents to the swarm:

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

Orchestrate Tasks

Distribute tasks across the swarm:

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

Monitor & Scale Swarms

// 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"

})

Workflow Automation

Create Workflow

Define event-driven workflows with message queue processing:

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

Execute Workflow

Run workflows synchronously or asynchronously:

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

Monitor Workflows

// 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"

})

Agent Assignment

Intelligently assign agents to workflow tasks:

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

Queue Management

Monitor and manage message queues:

mcp__flow-nexus__workflow_queue_status({

  queue_name: "optional-specific-queue",

  include_messages: true // Show pending messages

})

Agent Orchestration

Full-Stack Development Pattern

// 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"]

  }

})

Research & Analysis Pattern

// 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"

})

CI/CD Pipeline Pattern

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

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

  }

})

Templates & Patterns

Use Pre-built Templates

// 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

Custom Template Creation

Save successful swarm configurations as reusable templates for future projects.

Advanced Features

Real-time Monitoring

// 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"

})

Swarm Metrics & Analytics

// 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

})

Multi-Swarm Coordination

Coordinate multiple swarms for complex, multi-phase projects:

// 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

})

Best Practices

1. Choose the Right Topology

// 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 })

2. Optimize Agent Assignment

// Use vector similarity for optimal matching

mcp__flow-nexus__workflow_agent_assign({

  task_id: "complex-task",

  use_vector_similarity: true

})

3. Implement Proper Error Handling

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"

  }

})

4. Monitor and Scale

// 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 })

}

5. Use Async Execution for Long-Running Workflows

// 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 })

6. Clean Up Resources

// Destroy swarm when complete

mcp__flow-nexus__swarm_destroy({ swarm_id: "id" })

7. Leverage Templates

// Use proven templates instead of building from scratch

mcp__flow-nexus__swarm_create_from_template({

  template_name: "code-review",

  overrides: { maxAgents: 4 }

})

Integration with Claude Flow

Flow Nexus swarms integrate seamlessly with Claude Flow hooks:

# Pre-task coordination setup

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

# Post-task metrics export

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

Common Use Cases

1. Multi-Repo Development

• Coordinate development across multiple repositories

• Synchronized testing and deployment

• Cross-repo dependency management

2. Research Projects

• Distributed information gathering

• Parallel analysis of different data sources

• Collaborative synthesis and reporting

3. DevOps Automation

• Infrastructure as Code deployment

• Multi-environment testing

• Automated rollback and recovery

4. Code Quality Workflows

• Automated code review

• Security scanning

• Performance benchmarking

5. Data Processing

• Large-scale ETL pipelines

• Real-time data transformation

• Data validation and quality checks

Authentication & Setup

# Install Flow Nexus

npm install -g flow-nexus@latest

# Register account

npx flow-nexus@latest register

# Login

npx flow-nexus@latest login

# Add MCP server to Claude Code

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

Support & Resources

• 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.