![Revisit consent button](../images/body-df29beacd7496220cc94.svg) We value your privacy We use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking "Accept All", you consent to our use of cookies. Customise Reject All Accept All Customise Consent Preferences ![](../images/body-2159bb67f3de8ad870eb.svg) We use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below. The cookies that are categorised as "Necessary" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... Show more NecessaryAlways Active Necessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data. * Cookie BCTempID * Duration 10 minutes * Description No description available. * Cookie __cf_bm * Duration 1 hour * Description This cookie, set by Cloudflare, is used to support Cloudflare Bot Management. * Cookie AWSALBCORS * Duration 7 days * Description Amazon Web Services set this cookie for load balancing. * Cookie _cfuvid * Duration session * Description Cloudflare sets this cookie to track users across sessions to optimize user experience by maintaining session consistency and providing personalized services * Cookie li_gc * Duration 6 months * Description Linkedin set this cookie for storing visitor's consent regarding using cookies for non-essential purposes. * Cookie __hssrc * Duration session * Description This cookie is set by Hubspot whenever it changes the session cookie. The __hssrc cookie set to 1 indicates that the user has restarted the browser, and if the cookie does not exist, it is assumed to be a new session. * Cookie __hssc * Duration 1 hour * Description HubSpot sets this cookie to keep track of sessions and to determine if HubSpot should increment the session number and timestamps in the __hstc cookie. * Cookie wpEmojiSettingsSupports * Duration session * Description WordPress sets this cookie when a user interacts with emojis on a WordPress site. It helps determine if the user's browser can display emojis properly. * Cookie BCSessionID * Duration 1 year 1 month 4 days * Description Blueconic sets this cookie as a unique identifier for the BlueConic profile. * Cookie _octo * Duration 1 year * Description No description available. * Cookie logged_in * Duration 1 year * Description No description available. * Cookie __Secure-YEC * Duration past * Description YouTube sets this cookie to stores the user's video player preferences using embedded YouTube video * Cookie __eoi * Duration 6 months * Description Description is currently not available. * Cookie AWSALBTGCORS * Duration 7 days * Description No description available. * Cookie login-status-p * Duration past * Description Description is currently not available. * Cookie AWSALBTG * Duration 7 days * Description No description available. * Cookie csrf_token * Duration session * Description No description available. * Cookie token_v2 * Duration 1 day * Description Description is currently not available. * Cookie D * Duration 1 year * Description Description is currently not available. * Cookie PHPSESSID * Duration session * Description This cookie is native to PHP applications. The cookie stores and identifies a user's unique session ID to manage user sessions on the website. The cookie is a session cookie and will be deleted when all the browser windows are closed. * Cookie VISITOR_PRIVACY_METADATA * Duration 6 months * Description YouTube sets this cookie to store the user's cookie consent state for the current domain. * Cookie cookietest * Duration session * Description The cookietest cookie is typically used to determine whether the user's browser accepts cookies, essential for website functionality and user experience. * Cookie __Host-airtable-session * Duration 1 year * Description This cookie is used to enable us to integrate the services of Airtable. * Cookie __Host-airtable-session.sig * Duration 1 year * Description This cookie is used to enable us to integrate the services of Airtable. * Cookie m * Duration 1 year 1 month 4 days * Description Stripe sets this cookie for fraud prevention purposes. It identifies the device used to access the website, allowing the website to be formatted accordingly. * Cookie BIGipServer* * Duration session * Description Marketo sets this cookie to collect information about the user's online activity and build a profile about their interests to provide advertisements relevant to the user. * Cookie __cfruid * Duration session * Description Cloudflare sets this cookie to identify trusted web traffic. * Cookie _GRECAPTCHA * Duration 6 months * Description Google Recaptcha service sets this cookie to identify bots to protect the website against malicious spam attacks. * Cookie __Secure-YNID * Duration 6 months * Description Google cookie used to protect user security and prevent fraud, especially during the login process. * Cookie cookieyes-consent * Duration 1 year * Description CookieYes sets this cookie to remember users' consent preferences so that their preferences are respected on subsequent visits to this site. It does not collect or store any personal information about the site visitors. Functional Functional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features. * Cookie lidc * Duration 1 day * Description LinkedIn sets the lidc cookie to facilitate data center selection. * Cookie brw * Duration 1 year * Description No description available. * Cookie brwConsent * Duration 5 minutes * Description Description is currently not available. * Cookie WMF-Uniq * Duration 1 year * Description Description is currently not available. * Cookie loom_anon_comment * Duration 1 year * Description No description available. * Cookie loom_referral_video * Duration session * Description Description is currently not available. * Cookie VISITOR_INFO1_LIVE * Duration 6 months * Description A cookie set by YouTube to measure bandwidth that determines whether the user gets the new or old player interface. * Cookie yt-remote-connected-devices * Duration Never Expires * Description YouTube sets this cookie to store the user's video preferences using embedded YouTube videos. * Cookie ytidb::LAST_RESULT_ENTRY_KEY * Duration Never Expires * Description The cookie ytidb::LAST_RESULT_ENTRY_KEY is used by YouTube to store the last search result entry that was clicked by the user. This information is used to improve the user experience by providing more relevant search results in the future. * Cookie yt-remote-device-id * Duration Never Expires * Description YouTube sets this cookie to store the user's video preferences using embedded YouTube videos. * Cookie yt-remote-session-name * Duration session * Description The yt-remote-session-name cookie is used by YouTube to store the user's video player preferences using embedded YouTube video. * Cookie yt-remote-fast-check-period * Duration session * Description The yt-remote-fast-check-period cookie is used by YouTube to store the user's video player preferences for embedded YouTube videos. * Cookie yt-remote-session-app * Duration session * Description The yt-remote-session-app cookie is used by YouTube to store user preferences and information about the interface of the embedded YouTube video player. * Cookie yt-remote-cast-available * Duration session * Description The yt-remote-cast-available cookie is used to store the user's preferences regarding whether casting is available on their YouTube video player. * Cookie yt-remote-cast-installed * Duration session * Description The yt-remote-cast-installed cookie is used to store the user's video player preferences using embedded YouTube video. * Cookie cp_session * Duration 3 months * Description Codepen sets this cookie for Help systems found in the website. * Cookie loid * Duration 1 year 1 month 4 days * Description This cookie is set by the Reddit. The cookie enables the sharing of content from the website onto the social media platform. Analytics Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc. * Cookie __hstc * Duration 6 months * Description Hubspot set this main cookie for tracking visitors. It contains the domain, initial timestamp (first visit), last timestamp (last visit), current timestamp (this visit), and session number (increments for each subsequent session). * Cookie hubspotutk * Duration 6 months * Description HubSpot sets this cookie to keep track of the visitors to the website. This cookie is passed to HubSpot on form submission and used when deduplicating contacts. * Cookie _ga * Duration 1 year 1 month 4 days * Description Google Analytics sets this cookie to calculate visitor, session and campaign data and track site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognise unique visitors. * Cookie _ga_* * Duration 1 year 1 month 4 days * Description Google Analytics sets this cookie to store and count page views. * Cookie __Host-psifi.analyticsTrace * Duration 6 hours * Description Description is currently not available. * Cookie __Host-psifi.analyticsTraceV2 * Duration 6 hours * Description Description is currently not available. * Cookie _gh_sess * Duration session * Description GitHub sets this cookie for temporary application and framework state between pages like what step the user is on in a multiple step form. * Cookie YSC * Duration session * Description YSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages. * Cookie ajs_anonymous_id * Duration 1 year * Description This cookie is set by Segment to count the number of people who visit a certain site by tracking if they have visited before. * Cookie vuid * Duration 1 year 1 month 4 days * Description Vimeo installs this cookie to collect tracking information by setting a unique ID to embed videos on the website. Performance Performance cookies are used to understand and analyse the key performance indexes of the website which helps in delivering a better user experience for the visitors. * Cookie AWSALB * Duration 7 days * Description AWSALB is an application load balancer cookie set by Amazon Web Services to map the session to the target. * Cookie acq * Duration past * Description Description is currently not available. * Cookie acq.sig * Duration past * Description Description is currently not available. * Cookie ptc * Duration 2 years * Description No description available. Advertisement Advertisement cookies are used to provide visitors with customised advertisements based on the pages you visited previously and to analyse the effectiveness of the ad campaigns. * Cookie muc_ads * Duration 1 year 1 month 4 days * Description Twitter sets this cookie to collect user behaviour and interaction data to optimize the website. * Cookie guest_id_marketing * Duration 1 year 1 month 4 days * Description Twitter sets this cookie to identify and track the website visitor. * Cookie guest_id_ads * Duration 1 year 1 month 4 days * Description Twitter sets this cookie to identify and track the website visitor. * Cookie personalization_id * Duration 1 year 1 month 4 days * Description Twitter sets this cookie to integrate and share features for social media and also store information about how the user uses the website, for tracking and targeting. * Cookie guest_id * Duration 1 year 1 month 4 days * Description Twitter sets this cookie to identify and track the website visitor. It registers if a user is signed in to the Twitter platform and collects information about ad preferences. * Cookie bcookie * Duration 1 year * Description LinkedIn sets this cookie from LinkedIn share buttons and ad tags to recognize browser IDs. * Cookie __Secure-ROLLOUT_TOKEN * Duration 6 months * Description YouTube sets this cookie to manage feature rollout and experimentation. It helps Google control which new features or interface changes are shown to users as part of testing and staged rollouts, ensuring consistent experience for a given user during an experiment. * Cookie yt.innertube::nextId * Duration Never Expires * Description YouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen. * Cookie yt.innertube::requests * Duration Never Expires * Description YouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen. * Cookie session_tracker * Duration session * Description This cookie is set by the Reddit. This cookie is used to identify trusted web traffic. It also helps in adverstising on the website. * Cookie edgebucket * Duration session * Description Reddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content. * Cookie did * Duration 1 year * Description Arbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year. Uncategorised Other uncategorised cookies are those that are being analysed and have not been classified into a category as yet. No cookies to display. Reject All Save My Preferences Accept All Toggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation [](https://towardsdatascience.com/) Publish AI, ML & data-science insights to a global community of data professionals. Sign in[Submit an Article](https://write.insightmediagroup.io) [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/) * * * [Write For TDS](https://towardsdatascience.com/submissions/) [LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search [Agentic AI](https://towardsdatascience.com/category/artificial-intelligence/agentic-ai/) # LangChain vs LangGraph: 4 Key Differences and When to Use Each A practical guide to choose the proper tool for your agentic workflows and systems [Soner Yıldırım](https://towardsdatascience.com/author/sonery/) August 13, 20265 min read ![](../images/000.jpg) Photo 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) LangChain 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. In 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. Let'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. ## 1. Pipeline vs Loops ![Image created with Gemini by author](../images/001.png) LangChain is a pipeline with a clear direction: Learn this step by step with the interactive [AI Agents](https://roadmap.sh/ai-agents) roadmap. ![](../images/002.png) We chain components together but in one direction, which looks like this in code: python ``` chain = prompt | model | parser output = chain.invoke(input) ``` We can still branch, run steps in parallel, and construct DAGs, but the default abstraction is data being moved forward through a pipeline. This structure is enough for solving many problems such as * Retrieve documents, then generate an answer * Extract fields, then save them * Summarize text, then classify it However, when it comes to sending backward, we need to write an outer Python loop. Hence, the application handles the rest, not LangChain. On the other hand, LangGraph treats loops as part of the workflow itself. It is basically a graph with nodes and edges. * A node performs a particular task. * A normal edge defines fixed transitions between nodes. * A conditional edge decides where to go next. Thanks 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: ![](../images/003.png) Customer 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. ## 2. Stateless vs Stateful A 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. This 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. We can manage all of that writing extra Python code but, as we mentioned earlier, it's not part of the chain. LangGraph 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: python ``` 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 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: python ``` def calculate_price_node(state: AgentState) -> dict[str, Any]: return {"calculated_price": calculate_price(state["booking_details"])} ``` LangGraph then merges that update into the existing state. State 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]`). Without a reducer, a new value normally replaces the old value, which is something we should avoid when keeping track of chat history. ## 3. Human-breaks-the-loop vs Human-in-the-loop We've witnessed agents making crucial mistakes. So, especially for some critical tasks, we may want to intervene with agents operations. Suppose 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. With a conventional LangChain pipeline, the common approach is to build that pause into the application surrounding the pipeline. A typical approach would be: 1. Run the chain until it proposes an action. 2. Save the proposal somewhere. 3. Return control to an API or job queue. 4. Wait for an approval event. 5. Reconstruct the required context. 6. Start the next portion of the workflow. This approach works but requires a lot of work. It sounds more like a human-breaks-the-loop and reconstructs it. On 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. When we trigger an interrupt, LangGraph saves the graph state so we don't need to worry about losing information or data. Dynamic interrupts can include a payload and resume with a human response. Here is an example: python ``` from langgraph.types import interrupt def approval_node(state: State): approved = interrupt({ "question": "Run this migration?", "sql": state["sql"], }) return {"approved": approved} ``` ## 4. Restarts vs Resume When 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. We 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. LangGraph 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. In order to activate it, we just need compile the graph with a checkpointer: python ``` graph.compile(checkpointer=checkpointer) ``` Checkpoints 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. We can also inspect the state before a problematic code or restart execution from any of the earlier checkpoints. ## When to use which We can stick with LangChain pipelines when the workflow is mostly predictable and forward-moving such as: * Standard RAG pipelines, * Simple question-answering bots, * Document extraction and classification tasks, * Summarization If the control flow is complex and constitutes the major part of our agentic system, we should consider using LangGraph. A typical use case would be coding assistants that generate, test, and repair code. LangGraph is also a better fit for workflows with repeated planning and evaluating actions. As we mentioned in the "restarts vs resume" section, applications that require pause, persist, and resume can advantage from the stateful LangGraph agents. Use LangChain when your application is best understood as a pipeline. Use LangGraph when it is better understood as a stateful system. Thank you for reading! ## Register Now to Continue Reading ### Unlock Towards Data Science with a Free Account All articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more. Register for freeSign in to your account * * * ![](../images/body-11c1a04e02d8a14729da.png) Written By ## [Soner Yıldırım](https://towardsdatascience.com/author/sonery/) [See all from Soner Yıldırım](https://towardsdatascience.com/author/sonery/) [Agentic Ai](https://towardsdatascience.com/tag/agentic-ai/)[Artificial Intelligence](https://towardsdatascience.com/tag/artificial-intelligence/)[Data Science](https://towardsdatascience.com/tag/data-science/) Share This Article * [](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) * [](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) * [](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) Towards Data Science is a community publication. Submit your insights to reach our global audience and earn through the TDS Author Payment Program. [Write for TDS](https://towardsdatascience.com/submissions/) ## Related Articles * [![](../images/004.jpg)](https://towardsdatascience.com/tool-calling-explained-how-ai-agents-decide-what-to-do-next/) ## [Tool Calling, Explained: How AI Agents Decide What to Do Next](https://towardsdatascience.com/tool-calling-explained-how-ai-agents-decide-what-to-do-next/) [Agentic AI](https://towardsdatascience.com/category/artificial-intelligence/agentic-ai/) Understanding how LLMs interact with the world around them, from returning data to taking action [Maria Mouschoutzi](https://towardsdatascience.com/author/m.mouschoutzi/) June 21, 202611 min read * [![](../images/005.jpg)](https://towardsdatascience.com/dont-let-claude-gaslight-you/) ## [Don’t Let Claude Grade Its Own Homework](https://towardsdatascience.com/dont-let-claude-gaslight-you/) [Agentic AI](https://towardsdatascience.com/category/artificial-intelligence/agentic-ai/) Cross-provider PR review with Codex in GitHub Actions, and why a second opinion from a different lab beats any self-review [Ruben Broekx](https://towardsdatascience.com/author/broekxruben/) July 15, 202622 min read * [![](../images/006.jpeg)](https://towardsdatascience.com/how-to-set-up-a-development-environment-for-machine-learning-b015a91bda8a/) ## [How to Set Up a Development Environment for Machine Learning](https://towardsdatascience.com/how-to-set-up-a-development-environment-for-machine-learning-b015a91bda8a/) [Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/) How to install, activate, and use a virtual environment for machine learning and data science-related tasks [Andrea D'Agostino](https://towardsdatascience.com/author/thedrewdag/) April 28, 20224 min read * [![](../images/007.png)](https://towardsdatascience.com/what-house-of-cards-got-right-and-wrong-about-data-science-41218f69c7f5/) ## [What House of Cards Got Right (and Wrong) About Data Science](https://towardsdatascience.com/what-house-of-cards-got-right-and-wrong-about-data-science-41218f69c7f5/) [Data Science](https://towardsdatascience.com/category/data-science/) Given the recent release of two House of Cards season 5 trailers and the impending release of season 5 of the binge-worthy Netflix series... [Matt Brems (he/him)](https://towardsdatascience.com/author/matthew-brems/) May 22, 20178 min read * [![](../images/008.webp)](https://towardsdatascience.com/when-to-use-one-model-and-when-to-use-a-team-of-agents/) ## [When to Use One Model and When to Use a Team of Agents](https://towardsdatascience.com/when-to-use-one-model-and-when-to-use-a-team-of-agents/) [Agentic AI](https://towardsdatascience.com/category/artificial-intelligence/agentic-ai/) When Codex is the right shape for the problem, when Claude Code is, and how I split 5 specialist agents between them on dense AI capacity work. [Naveen Goel](https://towardsdatascience.com/author/naveengoel/) September 14, 202616 min read * [![](../images/009.jpg)](https://towardsdatascience.com/hacking-statistical-significance-hypothesis-testing-with-ml-approaches-74ff102c5ff1/) ## [Hacking Statistical Significance: Hypothesis Testing with ML Approaches](https://towardsdatascience.com/hacking-statistical-significance-hypothesis-testing-with-ml-approaches-74ff102c5ff1/) [Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/) Test Statistical Significance in any Context Without Assumptions [Marco Cerliani](https://towardsdatascience.com/author/cerlymarco/) January 10, 20236 min read * [![](../images/010.jpg)](https://towardsdatascience.com/vice-presidential-and-presidential-debate-analysis-using-data-science-61d542f0c974/) ## [Vice Presidential and Presidential Debate Analysis using Data Science](https://towardsdatascience.com/vice-presidential-and-presidential-debate-analysis-using-data-science-61d542f0c974/) [Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/) Debate Analysis using Data Science: Using YouTube Comments to find the true intent of voters [Aadit Kapoor](https://towardsdatascience.com/author/aaditkapoor/) October 24, 20205 min read * [![](../images/011.jpg)](https://towardsdatascience.com/the-rise-of-semantic-entity-resolution/) ## [The Rise of Semantic Entity Resolution](https://towardsdatascience.com/the-rise-of-semantic-entity-resolution/) [Agentic AI](https://towardsdatascience.com/category/artificial-intelligence/agentic-ai/) Building knowledge graphs to power autonomous agents [Russell Jurney](https://towardsdatascience.com/author/rjurney/) September 14, 202518 min read * [![](../images/012.jpg)](https://towardsdatascience.com/plan-code-execute-designing-agents-that-create-their-own-tools/) ## [Plan–Code–Execute: Designing Agents That Create Their Own Tools](https://towardsdatascience.com/plan-code-execute-designing-agents-that-create-their-own-tools/) [Agentic AI](https://towardsdatascience.com/category/artificial-intelligence/agentic-ai/) The case against pre-built tools in Agentic Architectures [Partha Sarkar](https://towardsdatascience.com/author/partha_sarkar/) February 4, 20268 min read * [YouTube](https://www.youtube.com/c/TowardsDataScience) * [X](https://x.com/TDataScience) * [LinkedIn](https://www.linkedin.com/company/towards-data-science/) * [Threads](https://www.threads.net/@towardsdatascience) * [Bluesky](https://bsky.app/profile/towardsdatascience.com) [](https://towardsdatascience.com/) Your home for data science and AI. The world's leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals. Subscribe to Our Newsletter[Write For TDS](https://towardsdatascience.com/submissions/)•[About](https://towardsdatascience.com/about-towards-data-science-d691af11cc2f/)•[Advertise](https://contact.towardsdatascience.com/advertise-with-towards-data-science)•[Privacy Policy](https://towardsdatascience.com/privacy-policy/)•[Terms of Use](https://towardsdatascience.com/website-terms-of-use/) © Insight Media Group, LLC 2026•[The New Stack](https://thenewstack.io)•[roadmap.sh](https://roadmap.sh)•[Creator Network](https://insightmediagroup.io/creator-network) #### Recommended Articles Close * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500) Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1 ](https://towardsdatascience.com/javascript-fatigue-you-dont-need-js-to-build-chatgpt/ "Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1") * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500) The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs ](https://towardsdatascience.com/subset-sum-problem-solved-in-linear-time-for-dense-enough-inputs/ "The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs") * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500) Does Your Company Have a Data Strategy? ](https://towardsdatascience.com/does-your-company-have-a-data-strategy-e8944dbec4ae/ "Does Your Company Have a Data Strategy?") * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/4970/image?etag=1780565225463&width=500&height=500) Productivity Tips, Data Career Insights, and Other Recent Must-Reads ](https://towardsdatascience.com/productivity-tips-data-career-insights-and-other-recent-must-reads-5c47eb7a2fe0/ "Productivity Tips, Data Career Insights, and Other Recent Must-Reads")