Free tool / pattern ranking
Agent Orchestration Pattern Picker
Select your constraints and get a ranked recommendation across orchestrator-worker, pipeline, debate, swarm, and judge-panel designs, with a diagram for the top fit.
Ranked patterns
Use the ranking as a design review prompt
Pattern selection should follow task shape. If the picker ranks a pipeline above orchestrator-worker, it is telling you the task has enough stable structure to keep control deterministic. If it ranks debate or judge-panel highly, the real question is whether your judge rubric can separate signal from shared model blind spots.
Why are pipelines often ranked highly?
A fixed pipeline is easier to test, budget, and observe. It should beat autonomous delegation whenever the stages are known before runtime.
When should I choose orchestrator-worker?
Choose it when the subtask list depends on the input and workers can return verifiable artifacts from independent research, review, or production tasks.
When does debate make sense?
Debate or judge-panel patterns fit when independent critique can expose reasoning gaps and the system has a rubric for deciding which critique matters.
Why does the tool penalize swarms?
Swarm designs need strong shared state, stop conditions, and observability. Without those, the term often hides coordination risk instead of solving it.
Read next
Sources used on this page
Anthropic / 2024-12-19
Building effective agents
Defines workflows vs agents and recommends starting with the simplest solution that meets the task.
OpenAI / 2025
A practical guide to building agents
Recommends maximizing one agent first, then splitting for prompt complexity, tool overload, manager patterns, or handoffs.
Microsoft Research / 2024-08
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
Documents a framework for composing conversable agents with LLMs, tools, code, and human input.
Du et al. / 2023-05-23
Improving Factuality and Reasoning in Language Models through Multiagent Debate
Studies multiple model instances that propose, critique, and converge over debate rounds.
Hong et al. / 2023-08-01
MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework
Frames cascading hallucinations as a core risk and uses SOP-style workflows to reduce errors.
Li et al. / 2023-03-31
CAMEL: Communicative Agents for Mind Exploration of Large Language Model Society
Introduces role-playing communicative agents for studying cooperative behavior in multi-agent settings.