LangChain is the higher-level place to start when its ready-made agent approach fits your application. LangGraph is the lower-level choice when you need to define the workflow itself: its steps, shared state, routing, and points where it can pause or resume. They are related rather than unrelated alternatives: LangChain’s agent implementations use LangGraph primitives.
What is the difference between LangChain and LangGraph?
The main difference is the level of control. LangChain provides higher-level building blocks and an agent implementation intended to make common agent use cases easier to start. LangGraph lets developers build directly around graph primitives, deciding how an application’s workflow is organized and how it moves from one step to another.
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That distinction is about abstraction, not a strict either-or choice. LangChain agents use LangGraph primitives underneath; developers can also work directly in LangGraph when the higher-level agent abstraction does not provide the control they need.
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Start with LangChain when you want to assemble an agent from common building blocks and your application fits the provided agent abstraction. The official learning guide presents it as an easier starting point for simpler use cases, including semantic search, retrieval-augmented generation (RAG), SQL, and voice agents. See the LangChain agent documentation.
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This can also be a practical way to begin without designing every workflow transition yourself. If the application later needs more direct control, LangGraph’s primitives are the foundation used by LangChain’s agent implementations.
When should you use LangGraph directly?
Use LangGraph directly when the workflow—not just the agent’s building blocks—needs to be explicitly designed. Its model represents an application as nodes that perform discrete work, shared state that carries data between steps, and routing decisions that determine what runs next. The Thinking in LangGraph guide explains this structure and its design considerations.
Use explicit nodes and state
A node is a function for a discrete step. Nodes work with shared state, while graph transitions make the order and branching of work explicit. This structure is useful when developers need to control how work is divided, what information passes between steps, and which path follows a decision.
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Plan for persistence and human input
The guide demonstrates checkpointer-backed persistence and interrupt/resume patterns for applications that need to pause for human input and continue later. It also discusses retries and the trade-offs involved in choosing node boundaries: smaller, more granular steps can affect resilience and observability, but may also cause repeated work. These are workflow-design choices, not a guarantee that every graph automatically handles them in the same way.
How do they compare?
| Decision | LangChain | LangGraph |
|---|---|---|
| Level of abstraction | Higher-level agent building blocks and an agent implementation. | Lower-level graph primitives for shaping a workflow directly. |
| Best fit | Simpler agent use cases that fit the provided abstraction. | Custom workflows needing direct control over steps, shared state, and routing. |
| Control flow | Use the agent abstraction; its implementations use LangGraph primitives. | Define nodes, shared state, decisions, and transitions directly. |
| Persistence and human steps | The cited agent overview does not detail these capabilities for the high-level abstraction. | The guide demonstrates persistence with a checkpointer and interrupt/resume patterns for human input. |
| Relationship | Agent implementations use LangGraph primitives. | Can be used directly for deeper workflow customization. |
Can you use LangChain and LangGraph together?
Yes. LangChain’s agent implementations already use LangGraph primitives, so using LangChain does not mean taking an unrelated path. You can start with the higher-level agent approach when it fits and use LangGraph directly when you need to customize the workflow more deeply. The exact implementation depends on the application and the current framework APIs; the conceptual distinction does not, by itself, prescribe a migration path.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to choose
- Choose LangChain when a simpler agent use case fits its provided abstraction and you want a higher-level starting point.
- Choose LangGraph directly when you need to design and control workflow steps, shared state, routing, persistence, or human pause-and-resume behavior.
- Think of them as different levels in a related ecosystem, not mutually exclusive frameworks.
The distinction here reflects the official documentation’s conceptual guidance, not an independent benchmark or a version-specific API comparison. For implementation details, consult the current agent documentation and LangGraph workflow guide.
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