{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Soner Yıldırım",
  "title_listed": "LangChain vs LangGraph: 4 Key Differences and When to Use Each",
  "keywords": "",
  "domain": "大模型算法",
  "content_type": "深度技术解析",
  "is_paid_flag": "否",
  "url": "https://towardsdatascience.com/langchain-vs-langgraph-4-key-differences-and-when-to-use-each/",
  "publish_date": "2026-08-13",
  "corpus_stage": "",
  "notes": "",
  "id": "6f4114025118",
  "collected_at": "2026-10-02T09:19:51",
  "title": "LangChain vs LangGraph: 4 Key Differences and When to Use Each | Towards Data Science",
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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Agentic AI](https://towardsdatascience.com/category/artificial-intelligence/agentic-ai/)\n# LangChain vs LangGraph: 4 Key Differences and When to Use Each\nA practical guide to choose the proper tool for your agentic workflows and systems\n[Soner Yıldırım](https://towardsdatascience.com/author/sonery/)\nAugust 13, 20265 min read\n![](../images/000.jpg)\nPhoto by [Mika Baumeister](https://unsplash.com/@kommumikation?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) on [Unsplash](https://unsplash.com/photos/a-tug-boat-pulling-a-large-container-ship-3XjMwxUHx0Q?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)\nLangChain came to our lives long before LangGraph. This is a clear indication of LangGraph closing a gap. In other words, LangGraph does things that are more complicated or not possible to do with LangChain.\nIn this article, we'll go over 4 key differences between LangChain and LangGraph, and how they impact the code we write to build agentic workflows.\nLet's first mention that these are not competing tools. LangGraph is part of the LangChain ecosystem. It is kind of an extension built on top of LangChain.\n## 1. Pipeline vs Loops\n![Image created with Gemini by author](../images/001.png)\nLangChain is a pipeline with a clear direction:\nLearn this step by step with the interactive [AI Agents](https://roadmap.sh/ai-agents) roadmap.\n![](../images/002.png)\nWe chain components together but in one direction, which looks like this in code:\npython\n\n```\nchain = prompt | model | parser \noutput = chain.invoke(input)\n```\n\nWe can still branch, run steps in parallel, and construct DAGs, but the default abstraction is data being moved forward through a pipeline.\nThis structure is enough for solving many problems such as\n  * Retrieve documents, then generate an answer\n  * Extract fields, then save them\n  * Summarize text, then classify it\n\n\nHowever, when it comes to sending backward, we need to write an outer Python loop. Hence, the application handles the rest, not LangChain.\nOn the other hand, LangGraph treats loops as part of the workflow itself. It is basically a graph with nodes and edges. \n  * A node performs a particular task.\n  * A normal edge defines fixed transitions between nodes.\n  * A conditional edge decides where to go next.\n\n\nThanks to the normal and conditional edges, we can route back to earlier nodes without a hustle. Here is the diagram of a [customer service agent](https://towardsdatascience.com/i-replaced-a-15-minute-booking-process-with-a-langgraph-ai-agent/) I built with LangGraph:\n![](../images/003.png)\nCustomer represents the input node, AI Agent is the model, price and booking engines are the other nodes. We can go back and forth between nodes. \n## 2. Stateless vs Stateful\nA LangChain pipeline does not hold a state within itself. Each runnable usually receives an input and returns an output. State is passed forward in the form of a dictionary, message, or a custom object.\nThis is enough when each step needs only the previous step’s result. However, once we have a more complex workflow with loops or branches, it is on us to track the current draft, validation errors, conversation history, retry counts, etc.\nWe can manage all of that writing extra Python code but, as we mentioned earlier, it's not part of the chain.\nLangGraph creates stateful agents so the state is part of the graph. We declare a schema for the state, usually in the form of a `TypedDict` . For example, here is the state object of my customer service agent:\npython\n\n```\nclass AgentState(TypedDict):\n    messages: Annotated[list[AnyMessage], add_messages]\n    booking_details: BookingDetails\n    calculated_price: NotRequired[float | None]\n    time_options: NotRequired[list[TimeOption]]\n    selected_slot: NotRequired[TimeOption | None]\n    status: BookingStatus\n    booking_id: NotRequired[str | None]\n```\n\nA node does not have to reconstruct the entire state. It can do partial updates and LangGraph handles it smoothly. In this agent, we have a price engine node, which only updates the `calculated_price` in the agent state:\npython\n\n```\ndef calculate_price_node(state: AgentState) -> dict[str, Any]:\n    return {\"calculated_price\": calculate_price(state[\"booking_details\"])}\n```\n\nLangGraph then merges that update into the existing state.\nState fields can also have reduces for values written by multiple nodes or same node multiple times. For example, the `messages` field of the customer service agent state is updated after every customer message so we have a reduced (`add_messages` ) for this field ( `messages: Annotated[list[AnyMessage], add_messages]`).\nWithout a reducer, a new value normally replaces the old value, which is something we should avoid when keeping track of chat history. \n## 3. Human-breaks-the-loop vs Human-in-the-loop\nWe've witnessed agents making crucial mistakes. So, especially for some critical tasks, we may want to intervene with agents operations.\nSuppose an agent prepares a database migration, refund, or production deployment. We may want a human-in-the-loop to approve it before agent actually executes.\nWith a conventional LangChain pipeline, the common approach is to build that pause into the application surrounding the pipeline. A typical approach would be:\n  1. Run the chain until it proposes an action.\n  2. Save the proposal somewhere.\n  3. Return control to an API or job queue.\n  4. Wait for an approval event.\n  5. Reconstruct the required context.\n  6. Start the next portion of the workflow.\n\n\nThis approach works but requires a lot of work. It sounds more like a human-breaks-the-loop and reconstructs it. \nOn the other hand, LangGraph provides dynamic `interrupt()` calls inside nodes. Interrupts allow us to pause graph execution at specific points and wait for external input to continue. \nWhen we trigger an interrupt, LangGraph saves the graph state so we don't need to worry about losing information or data.\nDynamic interrupts can include a payload and resume with a human response. Here is an example:\npython\n\n```\nfrom langgraph.types import interrupt\n\ndef approval_node(state: State):\n    approved = interrupt({\n        \"question\": \"Run this migration?\",\n        \"sql\": state[\"sql\"],\n    })\n    return {\"approved\": approved}\n```\n\n## 4. Restarts vs Resume\nWhen a step in a normal chain fails, the simplest way to recover is often to invoke the chain again. This can be an expensive option because preceding model calls, retrieval queries, transformations, other tool calls need to be repeated.\nWe can add caching and write custom resume logic but those would not be part of the chain. Similar to our discussing in previous points, these are handled on the application level, not in LangChain.\nLangGraph has something called checkpointer, which is a state persistence layer that saves a snapshot of an agent's graph state at every step of execution. \nIn order to activate it, we just need compile the graph with a checkpointer:\npython\n\n```\ngraph.compile(checkpointer=checkpointer)\n```\n\nCheckpoints allows for resuming after a failure rather than restarting the entire workflow. This saves us the cost (both time and money) of executing the successful operations again.\nWe can also inspect the state before a problematic code or restart execution from any of the earlier checkpoints. \n## When to use which\nWe can stick with LangChain pipelines when the workflow is mostly predictable and forward-moving such as: \n  * Standard RAG pipelines,\n  * Simple question-answering bots,\n  * Document extraction and classification tasks,\n  * Summarization\n\n\nIf the control flow is complex and constitutes the major part of our agentic system, we should consider using LangGraph.\nA typical use case would be coding assistants that generate, test, and repair code.\nLangGraph is also a better fit for workflows with repeated planning and evaluating actions.\nAs we mentioned in the \"restarts vs resume\" section, applications that require pause, persist, and resume can advantage from the stateful LangGraph agents.\nUse LangChain when your application is best understood as a pipeline. Use LangGraph when it is better understood as a stateful system.\nThank you for reading!\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](../images/body-11c1a04e02d8a14729da.png)\nWritten By\n## [Soner Yıldırım](https://towardsdatascience.com/author/sonery/)\n[See all from Soner Yıldırım](https://towardsdatascience.com/author/sonery/)\n[Agentic Ai](https://towardsdatascience.com/tag/agentic-ai/)[Artificial Intelligence](https://towardsdatascience.com/tag/artificial-intelligence/)[Data Science](https://towardsdatascience.com/tag/data-science/)\nShare This Article\n  * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Flangchain-vs-langgraph-4-key-differences-and-when-to-use-each%2F&title=LangChain%20vs%20LangGraph%3A%204%20Key%20Differences%20and%20When%20to%20Use%20Each)\n  * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Flangchain-vs-langgraph-4-key-differences-and-when-to-use-each%2F&title=LangChain%20vs%20LangGraph%3A%204%20Key%20Differences%20and%20When%20to%20Use%20Each)\n  * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Flangchain-vs-langgraph-4-key-differences-and-when-to-use-each%2F&text=LangChain%20vs%20LangGraph%3A%204%20Key%20Differences%20and%20When%20to%20Use%20Each)\n\n\nTowards Data Science is a community publication. 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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Agentic AI](https://towardsdatascience.com/category/artificial-intelligence/agentic-ai/)\n# LangChain vs LangGraph: 4 Key Differences and When to Use Each\nA practical guide to choose the proper tool for your agentic workflows and systems\n[Soner Yıldırım](https://towardsdatascience.com/author/sonery/)\nAugust 13, 20265 min read\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/08/mika-baumeister-3XjMwxUHx0Q-unsplash-scaled-1.jpg)\nPhoto by [Mika Baumeister](https://unsplash.com/@kommumikation?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) on [Unsplash](https://unsplash.com/photos/a-tug-boat-pulling-a-large-container-ship-3XjMwxUHx0Q?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)\nLangChain came to our lives long before LangGraph. This is a clear indication of LangGraph closing a gap. In other words, LangGraph does things that are more complicated or not possible to do with LangChain.\nIn this article, we'll go over 4 key differences between LangChain and LangGraph, and how they impact the code we write to build agentic workflows.\nLet's first mention that these are not competing tools. LangGraph is part of the LangChain ecosystem. It is kind of an extension built on top of LangChain.\n## 1. Pipeline vs Loops\n![Image created with Gemini by author](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/08/image-30.png)\nLangChain is a pipeline with a clear direction:\nLearn this step by step with the interactive [AI Agents](https://roadmap.sh/ai-agents) roadmap.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/08/image-29-1024x112.png)\nWe chain components together but in one direction, which looks like this in code:\npython\n\n```\nchain = prompt | model | parser \noutput = chain.invoke(input)\n```\n\nWe can still branch, run steps in parallel, and construct DAGs, but the default abstraction is data being moved forward through a pipeline.\nThis structure is enough for solving many problems such as\n  * Retrieve documents, then generate an answer\n  * Extract fields, then save them\n  * Summarize text, then classify it\n\n\nHowever, when it comes to sending backward, we need to write an outer Python loop. Hence, the application handles the rest, not LangChain.\nOn the other hand, LangGraph treats loops as part of the workflow itself. It is basically a graph with nodes and edges. \n  * A node performs a particular task.\n  * A normal edge defines fixed transitions between nodes.\n  * A conditional edge decides where to go next.\n\n\nThanks to the normal and conditional edges, we can route back to earlier nodes without a hustle. Here is the diagram of a [customer service agent](https://towardsdatascience.com/i-replaced-a-15-minute-booking-process-with-a-langgraph-ai-agent/) I built with LangGraph:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/07/customer-service-agent.drawio.png)\nCustomer represents the input node, AI Agent is the model, price and booking engines are the other nodes. We can go back and forth between nodes. \n## 2. Stateless vs Stateful\nA LangChain pipeline does not hold a state within itself. Each runnable usually receives an input and returns an output. State is passed forward in the form of a dictionary, message, or a custom object.\nThis is enough when each step needs only the previous step’s result. However, once we have a more complex workflow with loops or branches, it is on us to track the current draft, validation errors, conversation history, retry counts, etc.\nWe can manage all of that writing extra Python code but, as we mentioned earlier, it's not part of the chain.\nLangGraph creates stateful agents so the state is part of the graph. We declare a schema for the state, usually in the form of a `TypedDict` . For example, here is the state object of my customer service agent:\npython\n\n```\nclass AgentState(TypedDict):\n    messages: Annotated[list[AnyMessage], add_messages]\n    booking_details: BookingDetails\n    calculated_price: NotRequired[float | None]\n    time_options: NotRequired[list[TimeOption]]\n    selected_slot: NotRequired[TimeOption | None]\n    status: BookingStatus\n    booking_id: NotRequired[str | None]\n```\n\nA node does not have to reconstruct the entire state. It can do partial updates and LangGraph handles it smoothly. In this agent, we have a price engine node, which only updates the `calculated_price` in the agent state:\npython\n\n```\ndef calculate_price_node(state: AgentState) -> dict[str, Any]:\n    return {\"calculated_price\": calculate_price(state[\"booking_details\"])}\n```\n\nLangGraph then merges that update into the existing state.\nState fields can also have reduces for values written by multiple nodes or same node multiple times. For example, the `messages` field of the customer service agent state is updated after every customer message so we have a reduced (`add_messages` ) for this field ( `messages: Annotated[list[AnyMessage], add_messages]`).\nWithout a reducer, a new value normally replaces the old value, which is something we should avoid when keeping track of chat history. \n## 3. Human-breaks-the-loop vs Human-in-the-loop\nWe've witnessed agents making crucial mistakes. So, especially for some critical tasks, we may want to intervene with agents operations.\nSuppose an agent prepares a database migration, refund, or production deployment. We may want a human-in-the-loop to approve it before agent actually executes.\nWith a conventional LangChain pipeline, the common approach is to build that pause into the application surrounding the pipeline. A typical approach would be:\n  1. Run the chain until it proposes an action.\n  2. Save the proposal somewhere.\n  3. Return control to an API or job queue.\n  4. Wait for an approval event.\n  5. Reconstruct the required context.\n  6. Start the next portion of the workflow.\n\n\nThis approach works but requires a lot of work. It sounds more like a human-breaks-the-loop and reconstructs it. \nOn the other hand, LangGraph provides dynamic `interrupt()` calls inside nodes. Interrupts allow us to pause graph execution at specific points and wait for external input to continue. \nWhen we trigger an interrupt, LangGraph saves the graph state so we don't need to worry about losing information or data.\nDynamic interrupts can include a payload and resume with a human response. Here is an example:\npython\n\n```\nfrom langgraph.types import interrupt\n\ndef approval_node(state: State):\n    approved = interrupt({\n        \"question\": \"Run this migration?\",\n        \"sql\": state[\"sql\"],\n    })\n    return {\"approved\": approved}\n```\n\n## 4. Restarts vs Resume\nWhen a step in a normal chain fails, the simplest way to recover is often to invoke the chain again. This can be an expensive option because preceding model calls, retrieval queries, transformations, other tool calls need to be repeated.\nWe can add caching and write custom resume logic but those would not be part of the chain. Similar to our discussing in previous points, these are handled on the application level, not in LangChain.\nLangGraph has something called checkpointer, which is a state persistence layer that saves a snapshot of an agent's graph state at every step of execution. \nIn order to activate it, we just need compile the graph with a checkpointer:\npython\n\n```\ngraph.compile(checkpointer=checkpointer)\n```\n\nCheckpoints allows for resuming after a failure rather than restarting the entire workflow. This saves us the cost (both time and money) of executing the successful operations again.\nWe can also inspect the state before a problematic code or restart execution from any of the earlier checkpoints. \n## When to use which\nWe can stick with LangChain pipelines when the workflow is mostly predictable and forward-moving such as: \n  * Standard RAG pipelines,\n  * Simple question-answering bots,\n  * Document extraction and classification tasks,\n  * Summarization\n\n\nIf the control flow is complex and constitutes the major part of our agentic system, we should consider using LangGraph.\nA typical use case would be coding assistants that generate, test, and repair code.\nLangGraph is also a better fit for workflows with repeated planning and evaluating actions.\nAs we mentioned in the \"restarts vs resume\" section, applications that require pause, persist, and resume can advantage from the stateful LangGraph agents.\nUse LangChain when your application is best understood as a pipeline. Use LangGraph when it is better understood as a stateful system.\nThank you for reading!\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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