flow-nexus-swarm

# 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. ## Self-Learning Intelligence Integrates with RuVector's Q-learning and vector memory for improved performance. CLI: `node .claude/intelligence/cli.js stats` ## 📋 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: ```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 ### Spawn Agents 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 ### Orchestrate Tasks 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 ### Monitor & Scale Swarms ```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" }) ``` ## Workflow Automation ### Create Workflow 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 ### Execute Workflow 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 ### Monitor Workflows ```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" }) ``` ### Agent Assignment 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 ### Queue Management Monitor and manage message queues: ```javascript mcp__flow-nexus__workflow_queue_status({ queue_name: "optional-specific-queue", include_messages: true // Show pending messages }) ``` ## Agent Orchestration ### Full-Stack Development Pattern ```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"] } }) ``` ### Research & Analysis Pattern ```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" }) ``` ### CI/CD Pipeline Pattern ```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 ```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"] },