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Cloud-based AI swarm deployment and event-driven workflow automation with Flow Nexus platform
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| name | flow-nexus-swarm |
| description | Cloud-based AI swarm deployment and event-driven workflow automation with Flow Nexus platform |
| tags | ["domain/skills","artifact/config","source/.claude"] |
Deploy and manage cloud-based AI agent swarms with event-driven workflow automation, message queue processing, and intelligent agent coordination.
Flow Nexus provides cloud-based orchestration for AI agent swarms with:
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:
Strategy Guide:
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:
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:
// 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"
})
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:
depends_onparallel: true for concurrent stepsRun 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:
// 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"
})
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:
Monitor and manage message queues:
mcp__flow-nexus__workflow_queue_status({
queue_name: "optional-specific-queue",
include_messages: true // Show pending messages
})
// 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"]
}
})
// 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"
})
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
}
})
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
}
})
// 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 swarmresearch-team: Research and analysis swarmcode-review: Automated code review swarmdata-pipeline: ETL and data processingSpecialized Templates:
ml-development: Machine learning project swarmmobile-dev: Mobile app developmentdevops-automation: Infrastructure and deploymentsecurity-audit: Security analysis and testingEnterprise Templates:
enterprise-migration: Large-scale system migrationmulti-repo-sync: Multi-repository coordinationcompliance-review: Regulatory compliance workflowsincident-response: Automated incident managementSave successful swarm configurations as reusable templates for future projects.
// 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"
})
// 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
})
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
})
// 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 })
// Use vector similarity for optimal matching
mcp__flow-nexus__workflow_agent_assign({
task_id: "complex-task",
use_vector_similarity: true
})
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"
}
})
// 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 })
}
// 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 })
// Destroy swarm when complete
mcp__flow-nexus__swarm_destroy({ swarm_id: "id" })
// Use proven templates instead of building from scratch
mcp__flow-nexus__swarm_create_from_template({
template_name: "code-review",
overrides: { maxAgents: 4 }
})
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"
# 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
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.
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