This question comes up constantly, and it's usually asked by teams who've built a LangChain prototype and hit a wall. Here's the short answer: they solve different problems. LangChain is a high-level framework. It gives you chains, prompt templates, retrieval systems, model connectors, and document loaders. It's excellent for building straightforward RAG applications, chatbots, and single-pass workflows. You connect a prompt to a model, add retrieval, and get a response. LangGraph is lower-level. It models your AI logic as a directed graph — nodes represent processing steps, edges define the conditions that determine which step runs next. This structure enables things LangChain alone struggles with: Stateful execution — a shared memory structure every node can read and update Conditional routing — different outputs lead down different paths Checkpointing — the workflow saves state after every step and resumes after failures Human-in-the-loop interrupts — pause points for review and approval Multi-agent coordination — supervisor agents routing tasks to specialized sub-agents Where this matters most is production reliability. A support workflow that handles refunds needs to check eligibility, verify the order, apply policy, and escalate edge cases. That's a graph, not a chain. A financial workflow that enforces compliance checkpoints before proceeding isn't possible with simple sequential prompts. The practical insight: LangGraph applications can use LangChain components internally. They're complementary, not competing. If you're moving from prototype to production and hitting limits around state, routing, or failure recovery, working with a team offering specialized LangGraph development services can save you from rebuilding your architecture mid-project. Start with LangChain if you're experimenting. Move to LangGraph when reliability matters.