LangChain and LangGraph are tools in the LangChain ecosystem for building agentic workflows. LangChain uses a pipeline model with unidirectional data flow, requiring external loops for backtracking. LangGraph treats workflows as graphs with nodes and edges, enabling bidirectional transitions. This allows handling complex control flows without manual loop management.

LangChain is stateless, passing state externally, while LangGraph maintains internal state via Typed Dict schemas. Nodes can update partial state, and reducers manage shared fields like conversation history. This makes LangGraph suitable for workflows needing memory, such as customer service agents.

Human intervention differs between the tools. LangChain requires application-level pauses for approvals, while LangGraph offers dynamic `interrupt()` calls within nodes. This pause-and-resume feature saves state and reduces restart costs. LangGraph also includes checkpointers for persisting state snapshots, enabling resuming workflows after failures.

Use LangChain for predictable, forward pipelines like RAG or summarization. Opt for LangGraph in complex, stateful systems with loops, branching, or human-in-the-loop requirements. LangGraph’s graph structure and state management make it ideal for coding assistants or iterative workflows.