/flow-nexus-swarm

name: flow-nexus-swarm
description: Cloud-based AI swarm deployment and event-driven workflow automation with Flow Nexus platform
category: orchestration
tags: [swarm, workflow, cloud, agents, automation, message-queue]
version: 1.0.0
requires:
  - flow-nexus MCP server
  - Active Flow Nexus account (register at flow-nexus.ruv.io)

Flow Nexus Swarm & Workflow Orchestration

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

📋 Table of Contents

1. [Overview](#overview) 2. [Swarm Management](#swarm-management) 3. [Workflow Automation](#workflow-automation) 4. [Agent Orchestration](#agent-orchestration) 5. [Templates & Patterns](#templates--patterns) 6. [Advanced Features](#advanced-features) 7. [Best Practices](#best-practices)

Overview

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

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-n