choosing-swarm-patterns

name	choosing-swarm-patterns
description	Use when coordinating multiple AI agents with Agent Relay's workflow engine and need to pick the right orchestration pattern - covers the 10 core patterns (fan-out, pipeline, hub-spoke, consensus, mesh, handoff, cascade, dag, debate, hierarchical) plus 14 specialized ones, with decision framework and accurate SDK/YAML examples.

Overview

The Agent Relay SDK (@agent-relay/sdk) supports 24 swarm patterns via a single swarm.pattern field. Patterns are configured declaratively in YAML or programmatically via the workflow() fluent builder — there are no standalone fanOut(...) / hubAndSpoke(...) helpers. Pick the simplest pattern that solves the problem; add complexity only when the system proves it's insufficient.

Two ways to run a pattern

1. YAML (portable):

import { runWorkflow } from "@agent-relay/sdk/workflows";

const run = await runWorkflow("workflows/feature-dev.yaml", {
  vars: { task: "Add OAuth login" },
});

Quick Decision Framework

```

Is the task independent per agent?
  YES → fan-out (parallel workers, hub collects)

Does each step need the previous step's output?
  YES → Is it strictly linear?
    YES → pipeline
    NO  → dag (parallel where possible, `dependsOn` edges)

Does a coordinator need to stay alive and adapt?
  YES → hub-spoke (single-level hub + workers)
        hierarchical (structurally identical in current impl; use for naming/intent)

Is the task about making a decision?
  YES → Do agents need to argue opposing sides?
    YES → debate (adversarial, full mesh)
    NO  → consensus (cooperative, full mesh + coordination.consensusStrategy)

Does the right specialist emerge during processing?
  YES → handoff (sequential chain, one active at a time)

Do all agents need to freely collaborate?
  YES → mesh (full peer-to-peer edges)

Is cost the primary concern?
  YES → cascade (chain of increasingly capable agents; each step's prompt
        decides whether to pass through or redo the prior output)

Pattern Reference (Core 10)

#	Pattern	Topology (actual edges)	Best For
1	fan-out	Hub broadcasts to N workers; workers reply to hub only	Independent subtasks (reviews, research, tests)
2	pipeline	Linear chain (agent_i → agent_{i+1})	Ordered stages (design → implement → test)
3	hub-spoke	Hub ↔ spokes (bidirectional); no spoke-to-spoke	Dynamic coordination, lead reviews/adjusts
4	consensus	Full mesh; decision via coordination.consensusStrategy	Architecture decisions, approval gates
5	mesh	Full mesh (every agent ↔ every other)	Brainstorming, collaborative debugging
6	handoff	Chain; passes control forward	Triage, specialist routing
7	cascade	Chain of dependsOn steps; all run on success, downstream skipped on upstream failure (no built-in "fall through")	Cost optimization: cheap first, each step's prompt passes through or redoes
8	dag	Edges from step dependsOn	Mixed dependencies, parallel where possible
9	debate	Full mesh (same topology as mesh; roles drive behavior)	Rigorous adversarial examination
10	hierarchical	Hub + subordinates (single-level in current impl)	Large teams; semantic distinction from hub-spoke

Heads up: hierarchical resolves to the same edge structure as hub-spoke in coordinator.ts:313-319. Multi-level tree topology is not currently implemented — use pattern name for intent, but expect the same runtime graph.

Additional Patterns (role-driven)

These 14 additional patterns exist in SwarmPattern (types.ts:114-139). The coordinator has role-based auto-selection heuristics (coordinator.ts:51-165), but they only fire when swarm.pattern is omitted — YAML validation requires it (runner.ts:2105-2117), so auto-selection is effectively a programmatic-API feature. In YAML, set swarm.pattern explicitly.

Topology is still resolved per-pattern once selected; the "Triggering roles" column reflects what the coordinator looks for to shape edges (per coordinator.ts:250-450):

Pattern	Roles the topology keys off	Topology
map-reduce	mapper + reducer	coordinator → mappers → reducers → coordinator
scatter-gather	—	hub → workers → hub
supervisor	supervisor	supervisor ↔ workers
reflection	critic or reviewer (auto-select uses critic only)	producers → critic → producers (loop)
red-team	attacker/red-team + defender/blue-team	adversarial mesh with optional judges
verifier	verifier	producers → verifiers → back to producers
auction	auctioneer	auctioneer → bidders → auctioneer
escalation	tier-*	tiered chain, escalate up / report down
saga	saga-orchestrator, compensate-handler	orchestrator ↔ participants
circuit-breaker	primary + fallback/backup	try primary, fallback on failure
blackboard	blackboard / shared-workspace	shared state hub
swarm	hive-mind / swarm-agent	stigmergy-style
competitive	— (declared explicitly)	independent parallel implementations + judge
review-loop	+ 2+

Structured Squad Review Loop

• Split the work into bounded implementation squads. Each squad owns a non-overlapping file or subsystem scope.
• Give each squad an implementer plus a shadow/review partner. The shadow follows the implementer in real time, checks alignment with the spec, and posts concise feedback before the work drifts.
• Require the implementer to self-reflect before external review: compare the final diff against the spec, AGENTS.md / CLAUDE.md, recent local conventions, tests, and declared non-goals.
• Run an independent self-review/fresh-eyes agent that reads the actual files and recent repo context, not just the chat transcript.
• Send that review back to the implementer for one repair round.
• After squads converge, run a final two-agent review team, usually one Claude reviewer and one Codex reviewer, independently. They compare notes, merge findings, and produce one final verdict.
• Spawn fresh fix agents for final-review findings. Those fix agents self-reflect, then the final reviewers re-check the post-fix state until the spec is fully satisfied or a blocker is documented.
• Use supervisor or hub-spoke when a lead needs to coordinate live squads.
• Use review-loop when the main risk is code quality and feedback iteration.
• Use reflection when critic feedback should loop directly back to producers.
• Use verifier when completion evidence matters more than design debate.
• Use competitive only when independent alternative implementations are useful; otherwise split by ownership scope.

Pattern Details

1. fan-out — Parallel Workers

await workflow("review")
  .pattern("fan-out")
  .agent("lead",     { cli: "claude", role: "lead" })
  .agent("auth-rev", { cli: "claude", role: "worker", interactive: false })
  .agent("db-rev",   { cli: "claude", role: "worker", interactive: false })
  .step("review-auth", { agent: "auth-rev", task: "Review auth.ts" })
  .step("review-db",   { agent: "db-rev",   task: "Review db.ts" })
  .run();

2. pipeline — Sequential Stages

swarm: { pattern: pipeline }
agents:
  - { name: designer,    cli: claude }
  - { name: implementer, cli: codex, interactive: false }
  - { name: tester,      cli: codex, interactive: false }
workflows:
  - name: build
    steps:
      - { name: design,    agent: designer,    task: "Design the API schema",
          verification: { type: output_contains, value: DONE } }
      - { name: implement, agent: implementer, dependsOn: [design],
          task: "Implement: {{steps.design.output}}" }
      - { name: test,      agent: tester,      dependsOn: [],
            }

3. hub-spoke — Persistent Coordinator

await workflow("api-build")
  .pattern("hub-spoke")
  .channel("swarm-api")
  .agent("lead",       { cli: "claude", role: "lead" })
  .agent("db-worker",  { cli: "claude", role: "worker" })   // interactive by default — hub DMs it
  .agent("api-worker", { cli: "claude", role: "worker" })   // interactive by default — hub DMs it
  .step("models",  { agent: "db-worker",  task: "Build database models" })
  .step("routes",  { agent: "api-worker", task: "Build route handlers", dependsOn: ["models"] })
  .step("review",  { agent: "lead",       task: "Review everything",    dependsOn: ["routes"] })
  .run();

4. consensus — Cooperative Voting

swarm: { pattern: consensus }
agents:
  - { name: perf,  cli: claude, role: reviewer }
  - { name: dx,    cli: claude, role: reviewer }
  - { name: sec,   cli: claude, role: reviewer }
coordination:
  consensusStrategy: majority   # declarative marker: majority | unanimous | quorum
  votingThreshold: 0.66
workflows:
  - name: decide
    steps:
      - { name: evaluate-perf, agent: perf, task: "Evaluate perf of Fastify migration" }
      - { name: evaluate-dx,   agent: dx,   task: "Evaluate DX of Fastify migration" }
      - { name: evaluate-sec,  agent: sec,  task:  }

5. mesh — Peer Collaboration

await workflow("debug-auth")
  .pattern("mesh")
  .channel("swarm-debug")
  .agent("logs",     { cli: "claude" })
  .agent("code",     { cli: "claude" })
  .agent("repro",    { cli: "claude" })
  .step("logs",  { agent: "logs",  task: "Check server logs" })
  .step("code",  { agent: "code",  task: "Review auth code" })
  .step("repro", { agent: "repro", task: "Write repro test" })
  .run();

6. handoff — Dynamic Routing

swarm: { pattern: handoff }
agents:
  - { name: triage,   cli: claude }
  - { name: billing,  cli: claude }
  - { name: tech,     cli: claude }
workflows:
  - name: support
    steps:
      - { name: triage,  agent: triage,  task: "Triage: {{request}}" }
      - { name: billing, agent: billing, dependsOn: [triage], task: "Handle billing" }
      - { name: tech,    agent: tech,    dependsOn: [triage], task: "Handle tech issues" }

7. cascade — Cost-Aware Fallthrough

await workflow("answer")
  .pattern("cascade")
  .agent("haiku",  { cli: "claude", model: "claude-haiku-4-5-20251001" })
  .agent("sonnet", { cli: "claude", model: "claude-sonnet-4-6" })
  .agent("opus",   { cli: "claude", model: "claude-opus-4-7" })
  .step("try-haiku",  { agent: "haiku",  task: "{{question}}" })
  .step("try-sonnet", { agent: "sonnet",
                        task: "If this is a complete answer, echo it verbatim. Otherwise answer anew:\n{{steps.try-haiku.output}}",
                        dependsOn: ["try-haiku"] })
  .step("try-opus",   { agent: "opus",
                        task: "Final-tier answer, using prior attempts for context:\n{{steps.try-sonnet.output}}",
                        dependsOn: ["try-sonnet"] })
  .run();

8. dag — Directed Acyclic Graph

await workflow("fullstack")
  .pattern("dag")
  .maxConcurrency(3)
  .agent("dev", { cli: "codex", role: "worker" })
  .step("scaffold",  { agent: "dev", task: "Create project scaffold" })
  .step("frontend",  { agent: "dev", task: "Build React UI", dependsOn: ["scaffold"] })
  .step("backend",   { agent: "dev", task: "Build API",       dependsOn: ["scaffold"] })
  .step("integrate", { agent: "dev", task: "Wire together",   dependsOn: ["frontend", "backend"] })
  .run();

9. debate — Adversarial Refinement

swarm: { pattern: debate }
agents:
  - { name: pro,   cli: claude, role: debater, task: "Argue FOR monorepo" }
  - { name: con,   cli: claude, role: debater, task: "Argue FOR polyrepo" }
  - { name: judge, cli: claude, role: judge,   task: "Decide after 3 rounds" }
coordination:
  barriers:
    - { name: debate-done, waitFor: [pro-round-3, con-round-3] }

10. hierarchical — Multi-Level (structurally hub-spoke today)

await workflow("large-team")
  .pattern("hierarchical")
  .agent("lead",     { cli: "claude", role: "lead" })
  .agent("fe-coord", { cli: "claude", role: "coordinator" })
  .agent("be-coord", { cli: "claude", role: "coordinator" })
  .agent("fe-dev",   { cli: "codex",  role: "worker", interactive: false })
  .agent("be-dev",   { cli: "codex",  role: "worker", interactive: false })
  .step("plan",    { agent: "lead",     task: "Coordinate full-stack app" })
  .step("fe-plan", { agent: "fe-coord", task: "Manage frontend",  dependsOn: ["plan"] })
  .step(, { : , : ,   : [] })
  .(, { : ,   : , : [] })
  .(, { : ,   : ,        : [] })
  .();

Verification & Completion Signals

An agent step can complete in several ways (runner.ts:5353-5395, runner.ts:4527-4538):

verification:
  type: output_contains   # or: exit_code | file_exists | custom
  value: DONE             # or: PLAN_COMPLETE, IMPLEMENTATION_COMPLETE, REVIEW_COMPLETE

Relaycast MCP — Correct Tool Names

The skill previously referenced mcp__relaycast__send / mcp__relaycast__dm — those names are wrong. The real tools (the first three are cited in the workflow convention-injection at relay-adapter.ts:31-35; the rest are exposed by the live relaycast MCP server):

Purpose	Tool	Source
Send DM to another agent	mcp__relaycast__message_dm_send	relay-adapter.ts:31
Check inbox	mcp__relaycast__message_inbox_check	relay-adapter.ts:35
List agents	mcp__relaycast__agent_list	relay-adapter.ts:35
Post to a channel	mcp__relaycast__message_post	relaycast MCP server
Reply in a thread	mcp__relaycast__message_reply	relaycast MCP server
Spawn sub-agent	mcp__relaycast__agent_add	relaycast MCP server
Remove sub-agent	mcp__relaycast__agent_remove	relaycast MCP server

interactive: false agents run as non-interactive subprocesses with no relay connection — they must NOT call any mcp__relaycast__* tool (validator warns on this at validator.ts:138-150, check NONINTERACTIVE_RELAY).

Reflection (Trajectories)

Reflection is not a reflectionThreshold callback. It's configured via the trajectories: block:

trajectories:
  enabled: true
  reflectOnBarriers: true   # config flag exists but runner does NOT currently invoke this path
  reflectOnConverge: true   # fires at parallel convergence points (runner.ts:2762-2779)
  autoDecisions: true       # record retry/skip/fail decisions

Common Mistakes

Mistake	Why It Fails	Fix
Using mesh/debate for everything	Full-mesh blows up message volume past ~5 agents	Use hub-spoke or dag for most tasks
Pipeline for independent work	Sequential bottleneck	Use fan-out or dag
Hub-spoke for 2 agents	Hub is unnecessary overhead	Use pipeline or fan-out
Expecting consensusStrategy to tally votes	Runner has no vote-tally logic; field only affects coordinator auto-selection	Aggregate votes in a judge/lead step that reads {{steps.*.output}}
Handoff with "routing = skip other branches"	Skipping only fires on upstream failure, not routing decisions	Emit a routing token in triage output; downstream prompts self-no-op if token doesn't match
Cascade expecting skip-on-success	Runner has no cascade skip logic; failed upstream skips downstream	Chain downstream prompts to pass-through or redo based on {{steps.previous.output}}
Relying on reflectOnBarriers	Config flag exists but runner never calls it	Use reflectOnConverge for convergence reflection; use reflection pattern for critic loops
interactive: false agent calling MCP	Non-interactive subprocess has no relay	Use interactive: true (default) or emit output on stdout
Relying on multi-level hierarchical	Topology is single-level hub in current impl	Use pattern for naming; model levels via dependsOn graph
Writing mcp__relaycast__send(...)	Wrong tool name	Use mcp__relaycast__message_post or message_dm_send

Resume & Re-run

```ts

// Resume a failed run:
await runWorkflow("feature-dev.yaml", { resume: "<runId>" });

// Skip ahead, re-using cached outputs from an earlier run:
await runWorkflow("feature-dev.yaml", {
  startFrom: "review",
  previousRunId: "<runId>",
});

Complete YAML Example

```yaml

version: "1.0"
name: feature-dev
description: "Blueprint-style feature development with quality gates."
swarm:
  pattern: hub-spoke
  maxConcurrency: 2
  timeoutMs: 3600000
  channel: swarm-feature-dev
  idleNudge: { nudgeAfterMs: 120000, escalateAfterMs: 120000, maxNudges: 1 }
agents:
  - { name: lead,      cli: claude, role: lead,     permissions: { access: full } }
  - { name: planner,   cli: codex,  role: planner,  interactive: false, permissions: { access: readonly } }
  - { name: developer, cli: codex,  role: worker,   interactive: false, permissions: { access:  } }
   {  ,   ,  ,  {   } }

    
     
    
       {  ,  ,   }
    
        
         
         
         {  ,   }
        
         
         []
         
         {  ,   }
        
         
         []
          
        
         
         []
         
         {  ,   }

  
     {  ,  [, , ],   }

   
   
   

   
   
   

Source of Truth

Claim	File
Pattern enum (24 patterns)	packages/sdk/src/workflows/types.ts:114-139
Topology resolution per pattern	packages/sdk/src/workflows/coordinator.ts:240-450
Interactive-only topology edges	packages/sdk/src/workflows/coordinator.ts:218-237
Pattern auto-selection heuristics (programmatic API only)	packages/sdk/src/workflows/coordinator.ts:51-165
WorkflowBuilder fluent API	packages/sdk/src/workflows/builder.ts
runWorkflow(yamlPath, options)	packages/sdk/src/workflows/run.ts
YAML validation requires version + name + swarm.pattern	packages/sdk/src/workflows/runner.ts:2105-2117
MCP tool names cited in convention-injection	packages/sdk/src/relay-adapter.ts:29-36
Completion modes (verification / evidence / owner / process-exit)	packages/sdk/src/workflows/runner.ts:5353-5395, 4527-4538
Completion via PTY + summary fallback	packages/sdk/src/workflows/runner.ts:6600-6615
Downstream skip on upstream failure (not success)	packages/sdk/src/workflows/runner.ts:7057-7088, step-executor.ts:329-334
Trajectory reflection (only reflectOnConverge wired)	packages/sdk/src/workflows/runner.ts:2762-2779, trajectory.ts:173-190