langgraph-fundamentals

To build stateful agentic workflows with LangGraph, define a StateGraph containing discrete task nodes and wire them using static or conditional edges. This architecture provides fine-grained control over execution flow and complex agent orchestration.

Handling errors during LangGraph graph execution involves implementing targeted strategies for transient failures, LLM-recoverable issues, and user-fixable errors. This ensures robust agent recovery and continuous workflow operation across complex multi-step processes.

Implement conditional routing for parallel tasks in LangGraph by utilizing the Send object for parallel worker orchestration and the Command object for combined state updates and routing. This enables advanced control over complex agent workflows.

LangGraph agents often get stuck in execution loops due to improperly configured conditional edges or inadequate state update strategies. Applying specific loop management techniques and refining node function signatures resolves these graph execution issues.

Yes, you can stream outputs and update state dynamically in a LangGraph workflow. The framework supports invoke streaming and specific state update strategies, allowing real-time data processing and adaptive agent orchestration.