dreamlab-ai Advanced Swarm Orchestration
Master advanced swarm patterns for distributed research, development, and testing workflows. This skill covers comprehensive orchestration strategies using both MCP tools and CLI commands.
When Not To Use
- For queen-led hierarchical coordination with Byzantine consensus -- use the hive-mind-advanced skill instead
- For cloud-based swarm deployment with Flow Nexus platform (cloud-based swarm: requires Flow Nexus account -- not currently installed)
- For sequential pipeline processing where output chains between steps -- use the stream-chain skill instead
- For full development + quality engineering with 111+ agents -- use the build-with-quality skill instead
- For GitHub-specific PR review coordination -- use the github-code-review skill instead
- For structured development workflows with 17 SPARC modes -- use the sparc-methodology skill instead
- For Byzantine fault-tolerant consensus -- use the hive-mind-advanced skill instead
Claude Code only: the mcp__claude-flow__* tool calls below need the claude-flow
MCP server registered (already wired in this container). On Codex / GPT-6 Astra:
use the CLI form instead — the claude-flow binary is on PATH, no MCP needed.
Quick Start
Prerequisites
The claude-flow CLI (ruflo) is already baked on PATH in this image — no install
step needed. If registering the MCP server in a fresh environment, point it at
the baked binary, not npx (npx claude-flow resolves a separate, older cached
package — verified: pinned PATH binary reports ruflo v3.38.21, npx claude-flow --version reports a different ruflo v3.5.75):
claude mcp add claude-flow claude-flow mcp start
Basic Pattern
// 1. Initialize swarm topology
mcp__claude-flow__swarm_init({ topology: "mesh", maxAgents: 6 })
// 2. Spawn specialized agents
mcp__claude-flow__agent_spawn({ agentType: "researcher", name: "Agent 1" })
// 3. Orchestrate tasks
mcp__claude-flow__task_orchestrate({ task: "...", strategy: "parallel" })
CLI Alternative
claude-flow swarm start -o "your task here" -s research -p
Core Concepts
Swarm Topologies
| Topology | Structure | Best For |
|---|---|---|
| Mesh | Peer-to-peer, all agents communicate directly | Research, analysis, brainstorming |
| Hierarchical | Coordinator with subordinates | Development, structured workflows |
| Star | Central coordinator, parallel spokes | Testing, validation, QA |
| Ring | Sequential processing chain | Multi-stage pipelines |
Agent Strategies
- Adaptive - Dynamic adjustment based on task complexity
- Balanced - Equal distribution of work across agents
- Specialized - Task-specific agent assignment
- Parallel - Maximum concurrent execution
Swarm Patterns Overview
See references/patterns.md for detailed topology examples including full agent configuration and multi-phase workflows for each pattern below.
Pattern 1: Research Swarm (Mesh)
- 6 agents: 2 researchers, 2 analysts, 1 documenter
- Phases: Gather → Validate → Knowledge graph → Report
- Key tools:
parallel_execute,memory_usage,neural_patterns
Pattern 2: Development Swarm (Hierarchical)
- 8 agents: architect, 2 coders, db engineer, 2 testers, reviewer, devops
- Phases: Design → Parallel implement → Test → Review/Deploy
- Key tools:
task_orchestrate,parallel_execute,workflow_execute
Pattern 3: Testing Swarm (Star)
- 7 agents: 4 test specialists, security tester, analyst, documenter
- Phases: Plan → Parallel execute → Performance/Security → Report
- Key tools:
parallel_execute,bottleneck_analyze,performance_report
Pattern 4: Analysis Swarm (Mesh)
- 5 agents: 4 specialist analyzers, reporter
- Phases: Parallel analysis → Bottleneck detect → Report
- Key tools:
parallel_execute,bottleneck_analyze,performance_report
Valid MCP Tools Reference
The following mcp__claude-flow__* tools are available in this environment:
| Tool | Purpose |
|---|---|
swarm_init |
Initialize swarm with topology and agent count |
agent_spawn |
Spawn a named agent with type and capabilities |
task_orchestrate |
Assign and orchestrate tasks with strategy |
swarm_status |
Get current swarm health and agent status |
memory_usage |
Store/retrieve/search coordination memory |
memory_store |
Store a value to RuVector memory |
memory_search |
Semantic search over memory |
memory_retrieve |
Retrieve by key |
memory_list |
List memory entries |
neural_patterns |
Analyze/learn neural coordination patterns |
parallel_execute |
Execute tasks in parallel across agents |
performance_report |
Generate performance metrics report |
bottleneck_analyze |
Identify performance bottlenecks |
load_balance |
Balance task load across swarm agents |
sparc_mode |
Activate a SPARC methodology mode |
coordination_sync |
Synchronize state across swarm |
workflow_create |
Create a reusable named workflow |
workflow_execute |
Execute a named workflow |
github_repo_analyze |
Analyze a GitHub repository |
github_pr_manage |
Manage GitHub pull requests |
Tools NOT in this list (e.g. swarm_monitor, batch_process, quality_assess, pattern_recognize, neural_train, pipeline_create, daa_fault_tolerance, memory_persist, state_snapshot, metrics_collect, health_check, trend_analysis, automation_setup, cost_analysis) are ghost tools — not currently available. Use claude-flow CLI equivalents or swarm_status / performance_report as substitutes. See references/patterns.md for annotated examples.
Real-World Quick Examples
// Research AI trends, analyze findings, generate report
mcp__claude-flow__swarm_init({ topology: "mesh", maxAgents: 6 })
// Spawn: 2 researchers, 2 analysts, 1 synthesizer, 1 documenter
// Parallel gather → Analyze patterns → Synthesize → Report
// Build complete web application with testing
mcp__claude-flow__swarm_init({ topology: "hierarchical", maxAgents: 8 })
// Spawn: 1 architect, 2 devs, 1 db engineer, 2 testers, 1 reviewer, 1 devops
// Design → Parallel implement → Test → Review → Deploy
// Comprehensive security analysis
mcp__claude-flow__swarm_init({ topology: "star", maxAgents: 5 })
// Spawn: 1 coordinator, 1 code analyzer, 1 security scanner, 1 penetration tester, 1 reporter
// Identify and fix performance bottlenecks
mcp__claude-flow__swarm_init({ topology: "mesh", maxAgents: 4 })
// Spawn: 1 profiler, 1 bottleneck analyzer, 1 optimizer, 1 tester
Best Practices
Choosing the Right Topology
- Mesh: Research, brainstorming, collaborative analysis
- Hierarchical: Structured development, sequential workflows
- Star: Testing, validation, centralized coordination
- Ring: Pipeline processing, staged workflows
Agent Specialization
- Assign specific capabilities to each agent
- Avoid overlapping responsibilities
- Use coordinator agents for complex workflows
- Leverage
memory_usagefor agent-to-agent communication
Parallel Execution
- Identify independent tasks before calling
parallel_execute - Use sequential strategy for dependent tasks
- Monitor resource usage:
performance_report - Implement error handling with
swarm_statuschecks
Memory Management
- Use namespaces to organise memory (e.g.
research/,development/design) - Set appropriate TTL values (e.g. 604800 = 7 days, 2592000 = 30 days)
- Store decisions and findings continuously during workflows
- Retrieve context at session start for continuity
Error Recovery
try {
await mcp__claude-flow__task_orchestrate({ "task": "complex operation", "strategy": "parallel" })
} catch (error) {
const status = await mcp__claude-flow__swarm_status({})
if (status.healthy) {
await mcp__claude-flow__task_orchestrate({ "task": "retry failed operation", "strategy": "sequential" })
}
}
Troubleshooting
| Issue | Solution |
|---|---|
| Agents not coordinating | Check topology selection; verify memory_usage calls use consistent namespaces |
| Parallel execution failing | Verify task dependencies; check resource limits; add error handling |
| Memory persistence issues | Verify namespaces; check TTL settings; use memory_list to audit |
| Performance degradation | Use bottleneck_analyze; reduce agent count; run performance_report |
| Ghost tool error | See Valid MCP Tools table above; use CLI fallback instead |
Related Skills
sparc-methodology- Systematic development workflow (17 modes)hive-mind-advanced- Byzantine fault-tolerant queen-led coordinationgithub-code-review- Repository management and PR automationbuild-with-quality- 111+ agent QE pipelineperformance-analysis- Bottleneck detection and profiling for a running swarm
References
- Claude Flow Documentation
- Swarm Orchestration Guide
- references/patterns.md - Detailed topology examples
Version: 2.0.0 | Last Updated: 2025-10-19 | Skill Level: Advanced