LangGraph

You build stateful graph-based agent workflows by defining a TypedDict state, implementing node function interfaces, assembling the graph with add_node and add_edge calls, and running graph.compile() before invocation. This supports complex multi-step agent reasoning and tool usage.

The best way to structure conditional branching and loops in LangGraph agents is through stateful graph workflows. You assemble nodes and configure edge routing to support sequential, conditional, looping, and parallel execution scenarios for robust planning.

Yes, LangGraph supports human-in-the-loop checkpoints for multi-step agent reasoning. You integrate these checkpoints within stateful graph workflows to facilitate robust planning and execution alongside conditional branching and parallel branches.

You implement ReAct and Plan-and-Execute patterns by using built-in guidance within graph-based agent systems. You define state interfaces, construct graphs with node functions, and route edges to support these specific multi-step agent reasoning workflows.

Yes, you can use integrated RAG queries for on-demand knowledge access during both development and runtime. LangGraph provides structured workflow patterns and built-in RAG-backed guidance to facilitate robust planning and execution for graph-based agents.

To compile and execute a LangGraph-driven agent, you need to define a TypedDict-based state, implement node functions, and assemble the graph using add_node and add_edge calls. Graph compilation via graph.compile() is required before invocation.