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

• Overview

• Swarm Management

• Workflow Automation

• Agent Orchestration

• Templates & Patterns

• Advanced Features

• 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-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.