For a conventional tool-using agent, start with LangChain’s agent API. Build directly with LangGraph when you need to define the workflow’s steps, state, routing, or pause-and-resume behavior yourself. These are related approaches, not separate foundations: LangChain agent implementations use LangGraph primitives, so the choice is mainly how much workflow control your application needs.
How the two approaches relate
LangChain provides a higher-level agent abstraction for common patterns, including examples such as RAG and SQL agents. LangGraph lets you work more directly with the execution structure underneath: nodes that read and update shared state, connected by transitions and routing decisions. LangChain’s learning guide presents the agent API as an accessible starting point and direct LangGraph implementation as the route to deeper customization. LangChain learning guide
That means this is not a choice between an agent framework and an unrelated workflow engine. You can begin with the higher-level agent API and use direct graph construction when the application’s requirements call for more explicit control.
Choose based on the workflow you need to own
| Decision area | LangChain agent API | Direct LangGraph construction |
|---|---|---|
| Initial implementation | Good fit when a conventional tool-using agent covers the task and you prefer a higher-level starting point. | You model the workflow with nodes, shared state, and transitions. |
| Control flow | Use while the built-in agent behavior matches the application. | Use when you need workflow-specific stages, branching, retries, or routing. |
| State and inspection | Can keep a simple agent implementation concise. | Makes shared state and node boundaries explicit, which can help with debugging and recovery. |
| Pause, review, and resume | These behaviors can use underlying LangGraph primitives when configured. | Interruptions and checkpoint-backed continuation can be expressed directly in the graph. |
| Learning path | The official learning guide lists common agent tutorials, including RAG and SQL examples. | Use custom workflow tutorials when the ready-made agent abstraction does not provide enough control. |
The comparison describes the documented fit of each approach, not a guarantee that one will be faster or more reliable for every application. Your state requirements, persistence configuration, and failure-recovery design still matter.
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When the LangChain agent API is enough
Choose the agent API when the task is essentially: give a model access to tools, let it work toward a goal, and return a result. It is a sensible first implementation if you do not need to own every transition between steps. LangChain’s learning guide positions its agent implementations as an easy starting point for simpler use cases and includes RAG and SQL agent tutorials. See LangChain’s agent learning paths
- The task fits a familiar tool-using agent pattern.
- You do not need custom branching or workflow-specific routing.
- You want to test the application’s behavior before taking on explicit graph design.
Do not choose the higher-level API merely to avoid graph concepts if the application already needs a carefully controlled multi-step process. In that case, the abstraction may hide decisions you need to make explicitly.
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When to build directly with LangGraph
Direct graph construction is a stronger fit when the process has distinct stages, conditional transitions, or data that must be carried across steps. In LangGraph, a node is a function that receives the current state and returns updates. A typical design starts by identifying the workflow, dividing it into steps, deciding what state those steps share, and connecting the nodes with the required routing. LangGraph: Thinking in LangGraph
- Different conditions should send work down different paths.
- Intermediate results need to be represented in shared state across stages.
- You want visible workflow boundaries to help inspect, debug, or recover execution.
- A process needs a deliberate pause for review before continuing.
Explicit control is useful only when it serves a real requirement. A straightforward agent does not automatically benefit from a custom graph simply because graph construction offers more ways to organize a workflow.
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Designing human review and resume behavior
LangGraph’s documented human-review pattern uses a checkpointer to save execution state and a thread ID to identify the run. The workflow can pause at an interrupt, wait for a human decision, then resume with that input. This is useful when an application needs an approval or review boundary rather than uninterrupted tool execution. Read the checkpointer, interrupt, and resume guidance
LangChain agents use LangGraph primitives, so the need for a review pause does not by itself mean the higher-level API is impossible. The practical question is whether the agent abstraction gives you enough control to configure the interruption and continuation your application requires. Build directly with LangGraph when you need to make those points in the workflow explicit.
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Node granularity, checkpoints, and recovery
Node boundaries affect what the application can inspect and what work may need to be repeated after a failure. More, smaller nodes can create more checkpoint boundaries and make intermediate work easier to examine. If execution stops inside a node, however, that node’s work may need to run again. LangGraph’s guide also says that more nodes do not necessarily make execution slower because checkpoints are written asynchronously by default. Treat that as documented guidance, not a performance guarantee: storage, durability settings, and the application’s workload can change the trade-offs. LangGraph workflow design guidance
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical decision process
- Describe the task as a workflow. If it is a direct tool-using agent and the built-in agent behavior is sufficient, begin with the LangChain agent API.
- Identify decisions the application must control. If you need custom stages, conditional routes, retries, or explicit handoffs, sketch those steps and state as a graph.
- Specify what must survive between steps. Determine which intermediate values belong in shared state and whether execution needs checkpointing for recovery or continuation.
- Locate review boundaries. If a person must inspect or approve work mid-run, decide where execution pauses and what input lets it resume.
- Start with the smallest adequate abstraction. Move to direct graph construction when the requirements exceed what the agent API handles cleanly, rather than adding workflow machinery without a concrete need.
The official learning guide includes simple agent patterns, custom workflow material, and multi-agent tutorials that combine approaches. The choice need not be permanent: use the abstraction that fits the current workflow, then make more control explicit as requirements grow. Explore LangChain’s learning paths
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Keep Deep Agents distinct
Deep Agents is a separate harness built on LangChain building blocks and LangGraph tooling. Its overview describes features for complex multi-step tasks such as planning, filesystem-based context management, subagents, long-term memory, and human approval. Those capabilities belong to Deep Agents; they should not be assumed to come with the basic LangChain agent API or to be required for every LangGraph application. Deep Agents overview
Check the documentation for your language and version
The comparison here is about abstraction level and workflow design. The cited learning guide is for Python, while the LangGraph workflow guide and Deep Agents overview are JavaScript documentation. These sources do not establish a versioned, side-by-side matrix of current Python and JavaScript package versions, API compatibility, or migration steps. For implementation details, check the live API reference and release notes for the language and package versions your application will use.
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