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Orchestrate multi-model agents, run workflows, and validate outputs deterministically.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "agent-orchestrator" yet — see the docs or source repo.
Use agent-orchestrator to design a leader-worker flow: have a lead agent break down the task of creating a product launch plan, assign it to copywriting, competitor analysis, and scheduling worker agents, then merge the final result.
A structured multi-agent workflow configuration, task assignment steps, and a merged launch plan.
Use agent-orchestrator to run a code review workflow: first have one model generate review comments, then have a second model verify them, and finally apply deterministic validation rules to check coverage of security, performance, and maintainability.
Executed review results, verification conclusions, and a rule-based validation report.
Using agent-orchestrator, create a structured MCP pipeline for user research processing with four stages: issue classification, summary generation, risk tagging, and result validation, and explain the input and output of each stage.
A staged agent pipeline definition, responsibilities of each node, and executable input/output structures.
Orchestrate multi-agent workflows with parallel tasks, pipelines, scheduling, and peer review.
Run deterministic agent orchestration with task decomposition, subagents, and review feedback.
Orchestrate multiple AI agents in real time and monitor tasks and artifacts.
Coordinate multiple AI models and personas for review, debate, and ideation.
Orchestrate local multi-agent workflows with gated lifecycle, handoffs, and host continuation.
Turn compatible LLMs into CrewAI orchestrators for building and running multi-agent workflows.