Which capability is the strongest reason to swap a LangChain AgentExecutor for a LangGraph StateGraph?

**AgentExecutor** was LangChain's classic agent loop. You give it tools, a prompt, and an LLM, and it runs the think act observe cycle internally until it produces a final answer. The loop is hidden inside the executor, which is good for getting started and bad when you need to do anything custom mid-loop. **LangGraph StateGraph** flips that around. The loop becomes an explicit graph: each step is a node, transitions are edges, and you can add conditional edges that branch based on the current state. A **checkpointer** persists the full state at each step into a backing store (in-memory, SQLite, Postgres). That single change unlocks several capabilities AgentExecutor cannot offer. - **Human in the loop interrupts.** Pause before a sensitive tool runs, let a human approve or edit, then resume. - **Branching.** Route to different sub-graphs based on state. - **Crash recovery.** A process restart picks up the same thread from the last checkpoint. - **Time-travel debugging.** Inspect any past checkpoint and replay forward. The other answer choices are distractors. Both libraries support the same providers, call the LLM identically, and have identical token costs. The architectural difference is how the loop is exposed.

**Control over the agent loop** is the reason. AgentExecutor hides the loop inside the framework: it runs think act observe until the LLM emits a final answer, and you cannot pause, branch on state, persist progress, or resume after a crash. LangGraph's StateGraph makes the loop a first-class object, with explicit nodes, conditional edges, and a checkpointer. The checkpointer is the load-bearing piece. After every node runs, the full state object is written to a backing store (in-memory for dev, SQLite or Postgres for production). That single capability unlocks four things AgentExecutor cannot do: human in the loop interrupts (pause before sensitive tool calls), branching on state (different paths for different inputs), crash recovery (resume from the last checkpoint after a process restart), and time-travel debugging (replay from any past state). The other options are pure distractors. Provider count is identical because both libraries call models through the same `langchain-openai` and `langchain-anthropic` clients. Inference speed is set by the model, not the framework. Token deduplication is not a feature of either library. The architectural difference, and the only honest reason to migrate, is the loop becoming a graph you own.