What are common real-world use cases where LangGraph works better than a linear pipeline?

What you need to know

For each use case, name the graph feature that makes it a good fit:

What makes a use case a poor fit

• One prompt in, one answer out (summarise, translate, classify).
• A fixed sequence with no failure branch.
• High-volume, low-latency paths where every checkpoint write costs milliseconds you cannot spare.

A real-life example

A broadband provider's support bot remembers each customer across sessions. On Monday a customer says their router is a dual-band model and they prefer Hindi. The graph saves the transcript under thread_id="cust-4471-2026-09-21" with a Postgres checkpointer, and a remember node writes {"router": "dual-band", "language": "hi"} to a long-term Store under the namespace ("customers", "4471"). On Thursday, in a new conversation with a new thread, the bot reads the Store first, answers in Hindi and skips the "which router do you have?" question. The flow also branches: after two failed troubleshooting steps it books a technician visit, which needs a human dispatcher to confirm a slot — an interrupt() that may wait two hours. None of this fits a linear pipeline.

Follow-up questions to expect

• "When would you not use LangGraph for an agent?" — When create_agent with middleware already covers it, or when the task is a single call. Less framework means fewer moving parts.
• "How is short-term memory different from long-term memory here?" — Short-term is the thread's checkpointed state; long-term is a Store shared across threads, keyed by user.
• "What is the latency cost?" — Mostly the checkpoint write per super-step. With the default durability="async" it overlaps the next step; use "exit" if you only need saving at the end.