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LangChain is the higher-level starting point for building agents from common building blocks; LangGraph is the lower-level framework for defining a stateful workflow’s steps, shared state, and routing directly. They are related rather than separate alternatives: LangChain agent implementations use LangGraph primitives. Start with LangChain when its agent abstraction fits your use case, and use LangGraph directly when you need to shape and control the workflow itself.
What is the difference between LangChain and LangGraph?
The key difference is the level of control you work at. LangChain provides a higher-level agent abstraction intended to make common agent use cases easier to assemble. LangGraph provides primitives for building workflows directly, so you define the work as nodes, the shared state they operate on, and the transitions that determine what runs next.
This is not a choice between unrelated technologies. LangChain’s agent implementations use LangGraph primitives. You can use the higher-level agent path without designing the underlying graph yourself, or work directly with LangGraph when the agent abstraction does not give you enough control.
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Choose LangChain when you want a quicker start and its agent abstraction covers the shape of your application. The official learning guide describes it as an easy route for simpler use cases, including semantic search, retrieval-augmented generation, SQL, and voice agents. These are examples, not a limit on what LangChain can be used for.
In this approach, you work with the agent-level building blocks rather than defining every workflow step and transition yourself. That can reduce the amount of control-flow design you need to do up front. If the application later needs more customization, you can work directly with LangGraph primitives instead.
When is LangGraph the better fit?
Use LangGraph directly when your application needs a custom, stateful workflow and you need to decide how its steps connect. The core model is a graph: nodes perform discrete work, shared state carries information between them, and routing decisions determine what happens next. This makes the workflow structure explicit rather than leaving it inside a higher-level agent abstraction.
The official guide also demonstrates persistence through checkpointers and interrupt-and-resume patterns for human input. It discusses retries and an important design trade-off: smaller or more numerous node boundaries can affect resilience and observability, but may also mean repeated work. Those capabilities and trade-offs are useful when designing a workflow; they do not by themselves establish that direct LangGraph is the right choice for every agent.
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| Decision point | LangChain | LangGraph |
|---|---|---|
| Level of abstraction | Higher-level agent building blocks and agent implementation. | Lower-level primitives for shaping a workflow directly. |
| Good starting point | Simpler agent use cases that fit the provided abstraction. | Custom workflows needing direct control over steps, state, or routing. |
| How control flow is handled | Work through the agent abstraction; the agent implementation uses LangGraph primitives. | Define nodes, shared state, decisions, and transitions directly. |
| Persistence and human input | The consulted comparison documentation does not detail these capabilities for the high-level abstraction. | The guide demonstrates checkpointer-backed persistence and interrupt/resume patterns for human input. |
| Relationship | Agent implementations use LangGraph primitives. | Can be used directly when you need deeper customization. |
Can you use LangChain and LangGraph together?
Yes. LangChain agent implementations already use LangGraph primitives, so choosing LangChain does not mean choosing a separate agent path unrelated to LangGraph. The practical distinction is whether you rely on the higher-level agent implementation or build the workflow directly with LangGraph.
If your application’s needs change, the two approaches are not mutually exclusive at the ecosystem level. The documentation supports the relationship between LangChain agents and LangGraph primitives, but does not prescribe a particular migration process or guarantee that changing approaches requires no code changes.
A practical way to decide
- Begin with the workflow need. If you need a common agent use case and the agent abstraction fits, start with LangChain.
- Identify where you need control. If you must explicitly design state, steps, routing, persistence, or human pauses, assess whether direct LangGraph workflow design is appropriate.
- Account for workflow boundaries. In LangGraph, node granularity affects the balance among resilience, observability, and repeated work. Design boundaries around meaningful steps rather than adding nodes without a reason.
- Check current implementation details. Documentation may change; consult the current guides before relying on version-specific APIs or configuration.
Sources and scope
This distinction is based on the official LangChain Learn guide and Thinking in LangGraph guide. The documentation consulted presents conceptual guidance, not an independent benchmark or a quantitative performance comparison. Because the guides’ indexed crawls predate October 2026, check the current documentation for version-specific implementation details.
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