langgraph-workflows

To build stateful multi-step agent workflows, use LangGraph's StateGraph architecture to define nodes and conditional edges. This approach enables durable execution, allowing your agents to maintain context and survive failures across complex, multi-turn interactions.

Yes, you can pause an agent workflow for human approval using LangGraph's human-in-the-loop capabilities. This allows the state machine to halt execution, wait for external input or validation, and then resume the graph from the exact paused state.

The best way to structure conditional routing for multi-step agents is by using conditional edges within a LangGraph StateGraph. This pattern evaluates the current state and dynamically directs the execution flow to specific nodes, enabling complex ReAct agent behaviors and parallel execution.

Yes, LangGraph supports token streaming for agent responses. This framework provides first-class streaming capabilities, allowing you to stream tokens as they are generated, alongside offering full execution traces and replay capabilities for debugging.

You manage complex agent states by implementing subgraphs within your Python LangGraph workflows. Subgraphs allow you to nest StateGraph instances, modularizing intricate agent workflows and isolating state machines for parallel execution branches.