langgraph-workflows

Build stateful multi-agent LLM workflows with cyclic routing by using LangGraph to implement typed state, reducer semantics, and conditional edges for predictable state updates across specialized agents.

Human-in-the-loop approval in LLM pipelines works by inserting execution pauses into the graph, allowing human intervention before resuming, which is useful for verifying actions like processing refunds in customer support.

Implement persistent execution checkpoints for LLM applications using LangGraph to enable resumable long-running pipelines, execution replay, and time-travel debugging across production-grade workflows.

Yes, you can use LangGraph to coordinate supervisor and swarm agent systems by defining agent handoff patterns and routing logic for teams of specialized LLM agents within a stateful graph.

You need conditional routing in LLM workflows when processing requires cyclic logic, such as routing billing queries to a specialist agent or pausing for human approval before executing critical actions.

Use LangGraph instead of linear chains to eliminate the complexity of building stateful, cyclic LLM workflows, enabling advanced capabilities like real-time streaming, execution replay, and coordinated multi-agent handoffs.