langgraph-orchestration

Conditional routing in multi-agent workflows is implemented by defining dynamic decision-making rules that allocate tasks between agents, such as routing a conversation between a summarizer and a researcher based on the user's intent and state.

Cyclic workflows for iterative agent refinement are supported natively. You can create iterative processes where agents loop through tasks to refine outputs, enabling complex problem-solving and continuous state updates across multiple agent interactions.

Yes, langchain-openai is required alongside langgraph. These dependencies are necessary for graph construction and LLM integration, providing the foundational interfaces to connect language models within the orchestrated agent pipelines.

Human-in-the-loop capabilities are added by leveraging the framework's persistence and checkpointing features. This ensures continuity and recoverability of agent sessions, allowing processes to pause for human input and resume interrupted workflows.

Conditional routing is needed when dynamic task allocation is required, such as in an AI customer support system where different agents handle information retrieval, problem solving, or escalation based on the conversation's state.