Flow Nexus Swarm — Платформа для оркестровки роя AI-агентов

--- name: flow-nexus-swarm description: Cloud-based AI swarm deployment and event-driven workflow automation with Flow Nexus platform --- # 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: ```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"] }, { 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 ```javascript // 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 ```javascript // 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 ```javascript // 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: ```javascript // 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 ```javascript // 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 ```javascript // 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 ```javascript 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 ```javascript // 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 ```javascript // 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 ```javascript // Destroy swarm when complete mcp__flow-nexus__swarm_destroy({ swarm_id: "id" }) ``` ### 7. Leverage Templates ```javascript // 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: ```bash # 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 ```bash # 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.