What Sets Them Apart
LangChain is the broader application framework: prompt orchestration, model adapters, retrievers, tools, agents, and ecosystem glue. LangGraph is the graph-based runtime for stateful agent workflows, where steps can loop, branch, pause for human approval, and resume from checkpoints.
The practical choice is not “old versus new.” Most teams start with LangChain-style components, then reach for LangGraph when the agent needs durable control flow, explicit state, retries, human-in-the-loop review, or more predictable production behavior.
LangChain and LangGraph at a Glance
Choose LangChain when the project is mainly about connecting models to data, tools, retrievers, and APIs. It remains the biggest ecosystem in this pair and is often the fastest way to assemble RAG, evaluation, routing, and provider-agnostic LLM application pieces.
Choose LangGraph when the hard problem is orchestration. Its nodes, edges, state objects, and checkpointing model make agent behavior easier to reason about when the workflow is multi-step, adaptive, or long-running.
For many production teams, the best answer is both: LangChain for integrations and reusable components, LangGraph for the agent execution layer that coordinates those components safely.
State, Durability, and Control Flow
Linear chains work well for known sequences, but agents often need loops, branching, interruption, retries, and memory of what already happened. LangGraph exposes those concerns directly instead of hiding them inside ad hoc callbacks or fragile prompt logic.
That is why LangGraph stands out as the primary recommendation for most production agent workloads. It gives engineering teams a clearer place to model state transitions, checkpoint progress, inspect failures, and add human review before risky tool calls or irreversible actions.
Ecosystem, Learning Curve, and Production Readiness
LangChain still has the advantage in breadth: tutorials, integrations, community examples, and adjacent tooling such as LangSmith. It is also a better entry point for teams that need to build a conventional LLM app before deciding whether they really need graph orchestration.
LangGraph has a steeper mental-model shift because teams must think in graphs and state machines. The payoff is operational clarity: once the workflow is complex enough, the graph often becomes easier to maintain than a large pile of chained functions and conditional prompts.
The Bottom Line
Use LangChain when you need a broad LLM application framework and fast access to the ecosystem. Use LangGraph when you are building production agents that need explicit state, durable execution, branching, and human-in-the-loop control.