Multi-Agent Structures Catalog

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Overview

• Pipelines and DAGs — SequentialWorkflow, ConcurrentWorkflow, AgentRearrange, SwarmRearrange, GraphWorkflow, BatchedGridWorkflow, SpreadSheetSwarm. These let you describe the topology of execution explicitly, from a flat A→B→C line to a full directed acyclic graph with fan-out/fan-in, callbacks, and streaming. Use these when you already know the shape of the workflow.
• Routers and selectors — SwarmRouter, MultiAgentRouter, AgentRouter, ModelRouter, SkillOrchestra. These don’t run a fixed plan; they look at the incoming task and pick which agent(s) (or which model) should handle it. The selector itself is either an LLM (“boss”), an embedding match, or a skill-graph lookup. Use these when the input space is broader than any single agent’s competence.
• Hierarchies and delegation — HierarchicalSwarm, HierarchicalStructuredCommunicationFramework, HybridHierarchicalClusterSwarm, PlannerWorkerSwarm. A director or supervisor decomposes the task and delegates pieces to workers, then synthesizes. The variants differ in how strictly the communication protocol is defined and whether the workers themselves can cluster and talk peer-to-peer.
• Ensembles and consensus — MixtureOfAgents, SelfMoASeq, HeavySwarm, MajorityVoting, CouncilAsAJudge, LLMCouncil, DebateWithJudge. The shared assumption is that one model’s first answer is rarely the best answer. These structures sample multiple opinions and combine them — by aggregator synthesis, by vote, by judge ruling, or by structured adversarial debate.
• Dialogue and discussion — GroupChat, ForestSwarm, AdvisorSwarm, plus the named-ritual templates in multi_agent_debates.py (OneOnOneDebate, RoundTableDiscussion, PeerReviewProcess, TrialSimulation, and friends). These run scripted conversational patterns end-to-end so you don’t have to reimplement “moderated panel” or “academic peer review” by hand.
• Topology experiments — the *Swarm family in various_alt_swarms.py (CircularSwarm, MeshSwarm, FibonacciSwarm, SigmoidSwarm, etc.) and the matching functional helpers in swarming_architectures.py. These exist for research and exploration: what happens if you only activate agents at prime indices? Sinusoidal positions? They’re cheap to try because they share a tiny common interface.
• Self-improvement and auto-construction — PlannerGeneratorEvaluator, AutoSwarmBuilder, SocialAlgorithms. These build or refine swarms dynamically: a planner negotiates contracts with a generator and evaluator; a builder reads a high-level description and spits out a configured swarm; SocialAlgorithms lets you upload an entirely custom communication protocol over a fixed agent set.

1. Most structures take a List[Agent]. Mix providers freely — a GPT agent and a Claude agent and a local Llama agent can sit side by side in MixtureOfAgents or GroupChat. The structure doesn’t care; LiteLLM normalizes the calls.
2. SwarmRouter is the meta-entry point. If you’re not sure which structure to commit to, instantiate one and change swarm_type= later — you don’t have to rewrite the orchestration code.
3. Topology choice is a lever, not a guess. Sequential is cheapest and most deterministic. Concurrent is fastest end-to-end but loses ordering. Hierarchical pays an extra LLM call to the director in exchange for cleaner delegation. Ensembles pay N× tokens for variance reduction. Pick the trade-off, not the buzzword.

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Catalog

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Conclusion

1. Start with the simplest structure that could plausibly work. A SequentialWorkflow or ConcurrentWorkflow is usually enough for a first pass and forces you to confirm the underlying agents are doing their jobs before you add coordination overhead.
2. Reach for SwarmRouter when prototyping. Swapping swarm_type= between "SequentialWorkflow", "MixtureOfAgents", "HierarchicalSwarm", and "MajorityVoting" is a one-line change and a fast way to see which topology actually helps on your task.
3. Escalate to a heavier pattern only when you can name the failure it fixes. Adding CouncilAsAJudge because the single-agent answers are inconsistent across criteria is a good reason; adding it because “more agents is better” usually just buys variance and cost.
4. Treat the topology swarms in various_alt_swarms.py and swarming_architectures.py as a research playground. They share a tiny interface, are cheap to try, and are useful when you want to ask empirical questions like “does this task benefit from non-uniform agent activation?”
5. Reach for SocialAlgorithms or AutoSwarmBuilder only when nothing in the built-in set fits. Most production workloads land cleanly on one of the canonical patterns; reinventing the protocol or auto-generating the swarm is a last resort, not a default.