LangGraph

LangGraph is LangChain’s answer to the question chains can’t handle: what happens when your workflow needs to loop, branch, pause, or recover? Instead of a linear pipeline, you model your agent as a state graph — nodes are functions that read and update a shared state object, edges (including conditional edges) decide where control flows next. Cycles are first-class, which is exactly what an agentic loop is.

This graph-first design is why LangGraph has become the default choice for production agents in the LangChain ecosystem and beyond. Explicit state gives you things ad-hoc loops don’t: checkpointing (persist state mid-run and resume later), human-in-the-loop interrupts (pause before a risky tool call and wait for approval), time-travel debugging, and clean multi-agent topologies like supervisor and swarm patterns. Compared to CrewAI’s role-based crews or AutoGen’s conversation-driven flow, LangGraph is lower-level and more verbose — but you can see and control every transition, which matters when reliability is the requirement.

You’ll pip install langgraph, define a state schema (a TypedDict or Pydantic model), add nodes with graph.add_node(), wire them with add_edge and add_conditional_edges, then compile() into a runnable app. For the common case there’s create_react_agent, which gives you a tool-calling agent in one line. Attach a checkpointer for memory across turns, and stream events to watch the graph execute node by node.