LangChain helps you build agents with higher-level building blocks, LangGraph gives you finer control over stateful workflows, and LangSmith helps you trace, evaluate, deploy, and monitor applications. They serve different layers and can work together, but you do not need all three for every project.
What each product does
| Product | Primary role | Best fit | Relationship to the others |
|---|---|---|---|
| LangChain | Higher-level agent framework with prebuilt architectures and integrations for models and tools. | Getting an agent started when a ready-made architecture offers enough control. | Its agents use LangGraph primitives underneath. LangGraph overview |
| LangGraph | Lower-level runtime and orchestration framework for defining workflows, state, and transitions. | Stateful or long-running workflows that need customized control, including a mix of deterministic and model-driven steps. | Can be used without LangChain, though LangChain components are commonly used in its documentation. LangGraph overview |
| LangSmith | Engineering platform for tracing, evaluation, deployment, and production monitoring. | Teams that need visibility into runs and a way to assess or monitor application behavior. | Works with LangChain, LangGraph, other frameworks, and custom stacks; it is not a workflow engine that replaces LangGraph. What is LangSmith? |
LangChain: a higher-level starting point
Use LangChain when its agent abstractions and model and tool integrations cover the application’s needs. The trade-off is that a prebuilt architecture may not expose all the control a specialized workflow requires. LangChain’s learning materials present it as an easier starting point for common agent use cases, with LangGraph as an option when deeper customization is needed. LangChain learning resources
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LangGraph: explicit workflow and state
In LangGraph, a workflow is made of nodes connected through shared state and transitions. Its documented capabilities include persistence, streaming, durable execution, and pauses for human involvement. That makes it a fit when the flow must be designed and controlled explicitly rather than handled by a general-purpose agent loop. Thinking in LangGraph
LangSmith: visibility and improvement
LangSmith sits around an application’s development and production lifecycle. Traces help teams inspect individual runs; evaluations help assess outputs or changes; deployment and monitoring support operating applications. It can be used even when the application was built with a different framework or a custom stack. What is LangSmith? Evaluation types
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How they fit together in an application
A common arrangement is to build an agent with LangChain, use LangGraph when the application needs more deliberate orchestration, and use LangSmith to inspect and evaluate behavior. These are compatible layers, not mandatory stages: a simple LangChain agent may not need a custom graph, a standalone LangGraph workflow need not use LangChain, and LangSmith can serve applications built on other stacks.
The key distinction is responsibility: LangChain supplies higher-level building blocks, LangGraph manages workflow execution and state, and LangSmith provides operational visibility and tooling. LangChain agents’ use of LangGraph primitives explains why the first two can compose rather than being mutually exclusive alternatives. LangGraph overview
Which one should you use?
- Choose LangChain if a prebuilt agent architecture and its integrations give you enough control.
- Choose LangGraph if you need to define state, branching, pauses, persistence, or a blend of deterministic and agent-driven steps explicitly. LangChain’s documentation recommends it for advanced workflows requiring heavy customization and carefully controlled latency; that is guidance, not a guarantee of a particular performance result. LangGraph overview
- Consider LangSmith if you need to inspect runs, debug behavior, evaluate changes, or monitor application quality in production. What is LangSmith?
Make the choice by need, not by product count
- Start with abstraction: decide whether a ready-made agent loop is enough or whether you need to design the control flow yourself.
- Check the workflow: identify whether the application needs state across steps, long-running execution, persistence, branching, or human review.
- Assess operations: decide whether run-level traces, evaluation, deployment support, and ongoing monitoring are necessary for your team.
- Keep the stack flexible: use LangChain components where they help; LangGraph and LangSmith are not restricted to applications built entirely with LangChain. LangGraph overview What is LangSmith?
The practical distinction
Use the simplest layer that meets your requirements. LangChain is the convenient higher-level route; LangGraph is for more explicit, stateful orchestration; LangSmith is for understanding and improving behavior across development and production. A project may use one, combine two, or use all three, depending on its workflow and operational needs.
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