Multi-Agent Orchestration

To coordinate multiple AI agents across frameworks, you need an orchestration layer that generates coordinated routing and state-sharing patterns for delegation, sequencing, and parallelism. This approach applies pattern-based routing to maintain consistency across ADK, LangGraph, CrewAI, and OpenAI agents.

The best way to route tasks in a multi-agent workflow is using pattern-based routing for delegation, sequential, parallel, hierarchical, and mesh coordination. This routing mechanism directs tasks across agents while applying framework adapters to ensure seamless cross-framework collaboration.

Yes, you can share state and handle errors consistently when orchestrating OpenAI agents with LangGraph by applying dedicated state-sharing and error-handling patterns. These orchestration patterns maintain workflow consistency and manage failures across different framework agents.

RAG-enabled knowledge access guides multi-agent orchestration decisions by backing framework adapters with relevant knowledge retrieval. This mechanism supplies contextual information to the routing logic, ensuring agents receive accurate data to execute delegated tasks effectively.

You need hierarchical coordination for your AI agents when tasks require structured delegation and layered management across multiple frameworks. This orchestration pattern organizes agents into parent-child relationships to sequence complex workflows and maintain state consistency.

Yes, multi-agent orchestration works with ADK, LangGraph, CrewAI, and OpenAI agents simultaneously by generating a cross-framework routing plan. The orchestration engine applies framework adapters to translate coordination patterns across these distinct environments.