LangChain arrived in our development toolkit long before LangGraph, and that timing highlights a crucial evolution: LangGraph was designed to fill the gaps that LangChain left open. In essence, LangGraph handles tasks that are either more complex or outright impossible to achieve with LangChain alone.
In this article, we’ll break down four key differences between LangChain and LangGraph, and explore how these differences shape the code we write when building agentic workflows.
First, it’s important to clarify: these are not competing tools. LangGraph is a part of the LangChain ecosystem—an extension built on top of LangChain that enhances its capabilities.
1. Pipeline vs. Loops
LangChain is fundamentally a pipeline with a clear, unidirectional flow:
We chain components together in a single direction, which in code looks like this:
chain = prompt | model | parser
output = chain.invoke(input)
While you can branch, run steps in parallel, and build directed acyclic graphs (DAGs), the default abstraction pushes data forward through a linear pipeline.
This structure suffices for many common tasks, such as:
- Retrieve documents, then generate an answer
- Extract fields, then save them
- Summarize text, then classify it
However, when you need to send data backward—for instance, in a retry loop or a feedback cycle—you’re forced to write an external Python loop. The application logic, not LangChain, handles the backtracking.
LangGraph, on the other hand, treats loops as an inherent part of the workflow. It models the process as a graph consisting of nodes and edges:
- Node: Performs a specific task.
- Normal edge: Defines fixed transitions between nodes.
- Conditional edge: Determines the next node based on runtime conditions.
Thanks to these edge types, you can route back to earlier nodes without additional Python scaffolding. For example, here’s a diagram of a customer service agent built with LangGraph:
In this graph, the customer input node feeds into an AI agent node, which interacts with price and booking engine nodes. The agent can seamlessly move back and forth between nodes based on the conversation's current state.
2. Stateless vs. Stateful
A LangChain pipeline is stateless; each runnable receives an input and returns an output, passing state forward as a dictionary, message, or custom object. This works fine when every step only needs the previous step's result.
But in more complex workflows with loops or branches, you’re responsible for tracking things like the current draft, validation errors, conversation history, retry counts, and more. You can manage this with extra Python code, but that logic lives outside the chain—adding complexity and potential for bugs.
LangGraph creates stateful agents where the state is an integral part of the graph. You declare a schema for the state, typically using a TypedDict. Here’s the state object from the customer service agent:
class AgentState(TypedDict):
messages: Annotated[list[AnyMessage], add_messages]
booking_details: BookingDetails
calculated_price: NotRequired[float | None]
time_options: NotRequired[list[TimeOption]]
selected_slot: NotRequired[TimeOption | None]
status: BookingStatus
booking_id: NotRequired[str | None]
A node doesn’t need to reconstruct the entire state; it can perform partial updates, and LangGraph handles the rest seamlessly. For instance, a price engine node in our agent can update only the calculated_price field, leaving other state untouched.
3. Implicit vs. Explicit Control Flow
LangChain abstracts away control flow. The | operator chains runnables, and the framework decides how data moves through the pipeline. You have some control via RunnableBranch or RunnableParallel, but the logic is largely embedded in the chain definition.
LangGraph makes control flow explicit. The graph structure—nodes, edges, and conditional edges—is visualizable and auditable. You can see exactly when and why a transition happens, which is invaluable for debugging complex agents or explaining behavior to stakeholders. This explicitness is particularly useful in enterprise settings where traceability and maintainability are paramount.
4. Abstractions for Agentic Workflows
LangChain provides high-level abstractions like AgentExecutor, which orchestrate a ReAct-style loop (Thought → Action → Observation). While convenient, these abstractions are opinionated and can limit flexibility when you need custom behavior.
LangGraph, by contrast, offers lower-level primitives: you define the graph, nodes, and edges yourself, giving you full control over the agent's decision-making process. This granularity is essential for building sophisticated agents that require custom reasoning loops, multi-step tool use, or human-in-the-loop checkpoints.
By 2026, the ecosystem has matured: LangGraph has become the go-to choice for production-grade agents, while LangChain remains excellent for simpler, linear pipelines. The choice isn't about which is better—it's about matching the tool to the problem's complexity.
When to Use Which
Use LangChain when:
- Your workflow is linear or a DAG with clear input-to-output flow.
- You want quick prototyping with minimal boilerplate.
- Your state requirements are simple (e.g., passing a single message object).
Use LangGraph when:
- You need loops, branching, or dynamic routing.
- Your agent must maintain complex state across multiple interactions.
- You require fine-grained control over the control flow for debugging or compliance.
- You’re building production-grade agents with human-in-the-loop or complex tool orchestration.
In summary, LangChain and LangGraph serve different purposes within the same ecosystem. LangChain is your streamlined pipeline builder; LangGraph is your stateful graph engine for agentic workflows. Choose based on the complexity and requirements of your project—and remember, you can always start with LangChain and migrate to LangGraph as your needs evolve.