{"id":771,"date":"2026-07-03T13:42:01","date_gmt":"2026-07-03T08:12:01","guid":{"rendered":"https:\/\/vault.theoneleap.com\/?p=771"},"modified":"2026-07-03T14:00:13","modified_gmt":"2026-07-03T08:30:13","slug":"langchain","status":"publish","type":"post","link":"https:\/\/vault.theoneleap.com\/index.php\/2026\/07\/03\/langchain\/","title":{"rendered":"LangChain vs LangGraph: Which Framework Should You Learn for AI Agents in 2026?"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\"><strong><strong>Introduction<\/strong><\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">LangChain is a place to start for people who are new to this and for people who work. It helps you make LLM applications quickly. On the hand LangGraph is a good choice when you need to make things that can remember what they did before and that can do many things one, after the other.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">If you want to learn one thing and get a job quickly you should start with LangChain.. If you want to make really good agent systems you should learn LangGraph too after you know LangChain. LangChain and LangGraph are both important so you should know LangChain first. Then learn LangGraph.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong><strong>Key Takeaways<\/strong><\/strong><\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>LangChain is a broad framework for building LLM applications and agentic workflows, with many integrations and abstractions.<\/li>\n\n\n\n<li>LangGraph is designed for controllable, stateful, and graph-based agent orchestration with memory, persistence, streaming, and human-in-the-loop support.<\/li>\n\n\n\n<li>For students, LangChain is usually easier to learn first because it gives a wider foundation.<\/li>\n\n\n\n<li>For working professionals, LangGraph becomes especially valuable when projects need reliability, branching logic, and longer-running workflows.<\/li>\n\n\n\n<li>The best learning path is often LangChain first, then LangGraph for advanced agent architecture.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">\u2022\u00a0\u00a0\u00a0 If you want to get intelligence skills that are really good and work well it is           very important to know the difference, between using a model and building a whole system with the artificial intelligence.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong><strong>Table of Contents<\/strong><\/strong><\/h2>\n\n\n\n<ol start=\"1\" class=\"wp-block-list\">\n<li>Definition<\/li>\n\n\n\n<li>Why It Matters<\/li>\n\n\n\n<li>How It Works<\/li>\n\n\n\n<li>Core Components<\/li>\n\n\n\n<li>Tools Section<\/li>\n\n\n\n<li>Real Examples<\/li>\n\n\n\n<li>Comparison<\/li>\n\n\n\n<li>Best Practices<\/li>\n\n\n\n<li>Common Mistakes<\/li>\n\n\n\n<li>FAQs<\/li>\n\n\n\n<li>Final Summary<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\"><strong><strong>Definition<\/strong><\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">LangChain is a tool that helps people make applications using language models and other things. It has a way of doing things that makes it easier to work with. This new way is built on top of LangGraph. LangGraph is a tool that helps people make systems that can think like agents. It does this by using graphs which&#8217;re like maps that show how things are connected.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This makes it easier to control things like what&#8217;s happening when things go wrong and when people need to get involved. To put it simply LangChain helps you start working on things. LangChain is good for getting started. LangGraph is good, for dealing with things. LangGraph helps you manage things better.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Framework<\/strong><\/td><td><strong>What it is<\/strong><\/td><td><strong>Best for<\/strong><\/td><\/tr><tr><td><strong>LangChain<\/strong><\/td><td>A framework for building LLM apps and agent workflows<\/td><td>Fast prototyping, app development, beginner-to-intermediate AI builds<\/td><\/tr><tr><td><strong>LangGraph<\/strong><\/td><td>A graph-based orchestration framework for agents<\/td><td>Stateful workflows, reliability, multi-step agent systems<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<figure class=\"wp-block-image aligncenter size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"579\" src=\"https:\/\/vault.theoneleap.com\/wp-content\/uploads\/2026\/07\/LangChain-vs-LangGraph-1024x579.png\" alt=\"LangChain\" class=\"wp-image-772\" style=\"aspect-ratio:1.767548906789413;width:511px;height:auto\" srcset=\"https:\/\/vault.theoneleap.com\/wp-content\/uploads\/2026\/07\/LangChain-vs-LangGraph-1024x579.png 1024w, https:\/\/vault.theoneleap.com\/wp-content\/uploads\/2026\/07\/LangChain-vs-LangGraph-300x170.png 300w, https:\/\/vault.theoneleap.com\/wp-content\/uploads\/2026\/07\/LangChain-vs-LangGraph-768x435.png 768w, https:\/\/vault.theoneleap.com\/wp-content\/uploads\/2026\/07\/LangChain-vs-LangGraph.png 1536w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong><strong>Why It Matters?<\/strong><\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">This topic is really important because teams that work with intelligence do not just want to see examples of what the technology can do. They want systems that can get information use tools handle problems and work well when they are actually being used. The difference between saying &#8220;I made a chatbot&#8221; and &#8220;I made an artificial intelligence product that people can use&#8221; is now a deal when it comes to getting hired especially for students and people who want to work with artificial intelligence. That is why knowing about LangChain and LangGraph is not about the technology it is about your career.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For people who want to be an intelligence engineer or work with large language models or make artificial intelligence agents the tools you use affect how fast you can build a collection of work to show people and how much that work looks like what you would actually do, on the job. The way OneLeap teaches its classes is meant to fill this gap: you work on projects you learn in real time and you build systems that are good enough to use in the real world instead of just getting a certificate.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong><strong>How It Works?<\/strong><\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">LangChain helps you build apps by giving you pieces to connect models with prompts, retrievers, tools, memory and workflows. You can use it to make a question-answering app, a generation pipeline that uses retrieval or an assistant that can call APIs and tools.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">LangChain is useful when you want an app layer and do not want to build every integration from scratch. LangGraph works in a way. You model your app as a graph with states and transitions. Each node in the graph is a step, like retrieve, reason call a tool validate or ask a human for approval.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This structure makes it easy to build workflows, with branching, looping, recovery and memory that lasts across steps. LangGraph and LangChain are used for building types of applications<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Step<\/strong><\/td><td><strong>LangChain approach<\/strong><\/td><td><strong>LangGraph approach<\/strong><\/td><\/tr><tr><td><strong>Start<\/strong><\/td><td>Define chains, tools, or agents<\/td><td>Define a graph with nodes and edges<\/td><\/tr><tr><td><strong>Logic<\/strong><\/td><td>More abstract and easier to begin with<\/td><td>More explicit and controllable<\/td><\/tr><tr><td><strong>State<\/strong><\/td><td>Supported, but often less central<\/td><td>Core design principle<\/td><\/tr><tr><td><strong>Reliability<\/strong><\/td><td>Good for many apps<\/td><td>Stronger for long-running workflows<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong><strong>Core Components<\/strong><\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">LangChain is built around LLM abstractions, prompt management, retrieval, tool use, and agents. These components help you move from raw model calls to structured application design, which is essential when building customer-facing AI products. For many beginners, this is the first framework that makes AI development feel practical instead of purely theoretical.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">LangGraph adds state machines, graph transitions, checkpoints, memory, streaming, and human-in-the-loop patterns. Those components are especially useful when y LangChain is made up of some parts like LLM abstractions and prompt management. It also includes retrieval and tool use and agents. All these parts help you go from using a model to actually building something real. This is really important when you are making something that people will use.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For people who are just starting out LangChain is a help. It makes building AI products feel like something you can actually do not something you read about.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Then there is LangGraph. It adds some things like state machines and graph transitions. You also get checkpoints and memory and streaming. It has something called human-in-the-loop patterns. These things are really useful when you are working on something that&#8217;s complicated. Like when you have things that need to work together or when you are working in a place with a lot of rules. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Sometimes you need to try things a times before you get it right. LangGraph helps you with all that. It gets you closer, to using AI in real life.our workflow cannot be trusted to run in one shot, such as multi-agent orchestration, regulated environments, or tasks that need retries and review. In practice, this is the layer that moves you closer to production AI engineering.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Core area<\/strong><\/td><td><strong>LangChain<\/strong><\/td><td><strong>LangGraph<\/strong><\/td><\/tr><tr><td><strong>Prompting<\/strong><\/td><td>Strong support<\/td><td>Can be used inside nodes<\/td><\/tr><tr><td><strong>Retrieval<\/strong><\/td><td>Strong support for RAG patterns<\/td><td>Can orchestrate RAG workflows<\/td><\/tr><tr><td><strong>Agents<\/strong><\/td><td>High-level agent abstractions<\/td><td>Low-level control over agent steps<\/td><\/tr><tr><td><strong>Memory<\/strong><\/td><td>Supported through application patterns<\/td><td>State and persistence are central<\/td><\/tr><tr><td><strong>Human review<\/strong><\/td><td>Possible in app design<\/td><td>Built naturally into workflow design<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong><strong>Tools Section<\/strong><\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">When you are learning these frameworks the other tools that you use with them are just as important as the frameworks themselves. The<a href=\"https:\/\/theoneleap.com\/\" rel=\"nofollow noopener\" target=\"_blank\"> OneLeap<\/a> brochure talks about tools and technologies like Python, OpenAI, Claude, LangChain, LangGraph, LlamaIndex, FastAPI, Streamlit, Docker, Weaviate, Pinecone, FAISS, Chroma, MLflow, Kubernetes and more. This is important because when you are working with artificial intelligence you are not just using one tool you are using a bunch of different tools that work together. These tools are like a stack of blocks with models, retrieval, deployment and observability layers all working to make artificial intelligence work. The frameworks are a part of this but the other tools, like Python and OpenAI are also very important.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Category<\/strong><\/td><td><strong>Tools mentioned in the brochure<\/strong><\/td><\/tr><tr><td><strong>Core programming<\/strong><\/td><td>Python<\/td><\/tr><tr><td><strong>Frameworks<\/strong><\/td><td>LangChain, LangGraph, LlamaIndex<\/td><\/tr><tr><td><strong>Retrieval\/vector DB<\/strong><\/td><td>Pinecone, FAISS, Weaviate,<\/td><\/tr><tr><td><strong>App development<\/strong><\/td><td>FastAPI, Streamlit,<\/td><\/tr><tr><td><strong>Deployment and ops<\/strong><\/td><td>Docker, Kubernetes, MLflow, Azure, GCP<\/td><\/tr><tr><td><strong>Model ecosystem<\/strong><\/td><td>OpenAI, Claude, PyTorch, TensorFlow, Transformers<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">For a learner, this means LangChain or LangGraph should never be studied in isolation. They become valuable when combined with APIs, databases, vector search, deployment tools, and evaluation workflows.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong><strong>Real Examples<\/strong><\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A student who is making a Q\/A app can use LangChain to connect the parts that find information create prompts and get answers from models fast. On that same student can switch to LangGraph if the app needs to check things multiple times make decisions based on different paths or look over the answers before giving a final result. This is similar to how artificial intelligence projects grow from an idea to a real product.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A person who is working and making a research helper can use LangChain to try out ideas then move to LangGraph when the process gets more complicated like when they need to search for things summarize what they find check if it is correct try again if it is not and get help if needed. Another example is a system that uses agents to work together where it is important to keep track of what is happening make sure everything works together smoothly and resolve any problems that come up. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/theoneleap.com\/\" rel=\"nofollow noopener\" target=\"_blank\">OneLeap&#8217;s <\/a>brochure has a project that uses LangGraph and MCP to make a system, like this which shows how these skills are used in artificial intelligence projects.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Use case<\/strong><\/td><td><strong>Better fit<\/strong><\/td><td><strong>Why<\/strong><\/td><\/tr><tr><td><strong>Simple chatbot or RAG prototype<\/strong><\/td><td>LangChain<\/td><td>Faster setup and broad abstractions<\/td><\/tr><tr><td><strong>Stateful research assistant<\/strong><\/td><td>LangGraph<\/td><td>Better control of multi-step workflows<\/td><\/tr><tr><td><strong>Multi-agent orchestration<\/strong><\/td><td>LangGraph<\/td><td>Graph-based coordination and state handling<\/td><\/tr><tr><td><strong>Learning foundation for AI apps<\/strong><\/td><td>LangChain<\/td><td>Easier entry point for most learners<\/td><\/tr><tr><td><strong>Production workflow with branching<\/strong><\/td><td>LangGraph<\/td><td>Reliability and explicit control<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong><strong>Comparison Section<\/strong><\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">LangChain and LangGraph are not strict competitors in every sense, because they often solve different layers of the same problem. LangChain is broader and more beginner-friendly, while LangGraph is more specialized and gives deeper orchestration control. AWS also presents them together in agentic AI guidance, which is a useful sign that both are relevant in modern AI system design.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Aspect<\/strong><\/td><td><strong>LangChain<\/strong><\/td><td><strong>LangGraph<\/strong><\/td><\/tr><tr><td><strong>Learning curve<\/strong><\/td><td>Easier for most beginners<\/td><td>Slightly steeper<\/td><\/tr><tr><td><strong>Abstraction level<\/strong><\/td><td>Higher-level<\/td><td>Lower-level and more explicit<\/td><\/tr><tr><td><strong>Best use<\/strong><\/td><td>General LLM app development<\/td><td>Stateful agent orchestration<\/td><\/tr><tr><td><strong>Reliability control<\/strong><\/td><td>Good<\/td><td>Strong<\/td><\/tr><tr><td><strong>Human-in-the-loop<\/strong><\/td><td>Possible<\/td><td>Native pattern<\/td><\/tr><tr><td><strong>Production complexity<\/strong><\/td><td>Moderate<\/td><td>Better for complex systems docs<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<figure class=\"wp-block-image aligncenter size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"582\" src=\"https:\/\/vault.theoneleap.com\/wp-content\/uploads\/2026\/07\/The-best-open-source-frameworks-for-building-AI-agents-in-2026-1024x582.png\" alt=\"The best open source frameworks for building AI agents in 2026\" class=\"wp-image-773\" style=\"width:520px;height:auto\" srcset=\"https:\/\/vault.theoneleap.com\/wp-content\/uploads\/2026\/07\/The-best-open-source-frameworks-for-building-AI-agents-in-2026-1024x582.png 1024w, https:\/\/vault.theoneleap.com\/wp-content\/uploads\/2026\/07\/The-best-open-source-frameworks-for-building-AI-agents-in-2026-300x171.png 300w, https:\/\/vault.theoneleap.com\/wp-content\/uploads\/2026\/07\/The-best-open-source-frameworks-for-building-AI-agents-in-2026-768x437.png 768w, https:\/\/vault.theoneleap.com\/wp-content\/uploads\/2026\/07\/The-best-open-source-frameworks-for-building-AI-agents-in-2026.png 1536w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Which should you learn first?<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">For students and working professionals it is a good idea to learn LangChain first. This is because LangChain gives you a view of the AI app ecosystem. It also helps you build things faster. Once you understand things like prompts and retrieval and tools and agents LangChain makes LangGraph much easier to understand. It also makes LangGraph more meaningful.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"683\" src=\"https:\/\/vault.theoneleap.com\/wp-content\/uploads\/2026\/07\/A-simple-decision-path-for-choosing-where-to-start-1024x683.png\" alt=\"\" class=\"wp-image-774\" style=\"aspect-ratio:1.5;width:518px;height:auto\" srcset=\"https:\/\/vault.theoneleap.com\/wp-content\/uploads\/2026\/07\/A-simple-decision-path-for-choosing-where-to-start-1024x683.png 1024w, https:\/\/vault.theoneleap.com\/wp-content\/uploads\/2026\/07\/A-simple-decision-path-for-choosing-where-to-start-300x200.png 300w, https:\/\/vault.theoneleap.com\/wp-content\/uploads\/2026\/07\/A-simple-decision-path-for-choosing-where-to-start-768x512.png 768w, https:\/\/vault.theoneleap.com\/wp-content\/uploads\/2026\/07\/A-simple-decision-path-for-choosing-where-to-start.png 1536w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong><strong>Best Practices<\/strong><\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">When you are starting out learn about the problem you are trying to solve, not the framework. If you are doing something like getting information or making a basic assistant LangChain is a good place to start. If your project is more complicated and needs to be able to do things or try again if something goes wrong or if someone needs to be able to check on it then you should use LangGraph.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">You should build things that you can use in life not just follow tutorials. The people at OneLeap say that you should work on projects and make systems that can be used by real people and that you should make a portfolio of your work. This is a way to learn about LangChain and LangGraph.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">You should also make sure you can see what your project is doing and check if it is working correctly from the beginning. This is because when you make something with intelligence that people are actually going to use it needs to work consistently not just be a good idea.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong><strong>Common Mistakes<\/strong><\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">People often make a mistake when they try to learn LangGraph without understanding the basics of LLM applications and how to use the tools. This makes it really tough to learn LangGraph because it is like they are already supposed to know how AI applications work. Another mistake people make is using LangChain for every project even when the project really needs to be able to handle more complex situations and manage its own state.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A second mistake is only building demos that do not do much. When companies are hiring this can be a problem because it looks like the person does not really understand how to make something that can handle problems and work in the world. A third mistake is not thinking about the system, just the framework. You need to know about APIs and databases and how to get your project online and how to keep an eye on it or the framework is not enough.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong><strong>FAQs<\/strong><\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong><strong>Q1. What is the main difference between LangChain and LangGraph?<\/strong><\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">LangChain is a framework that helps you build applications with Large Language Models and agent workflows. LangGraph is more focused on organizing these agents in a way that&#8217;s reliable and easy to control. To put it simply LangChain helps you build things faster. Langgraph helps you control complicated processes better.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong><strong>Q2. Should beginners learn LangChain or LangGraph first?<\/strong><\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Most people who are just starting out should start with LangChain. This is because LangChain teaches you the basics of building AI applications without getting too complicated away. Once you understand LangChain, LangGraph is easier to learn and really useful for advanced things like handling different paths recovering from mistakes and working with multiple agents.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong><strong>Q3. Is LangGraph replacing LangChain?<\/strong><\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">No, LangChain and LangGraph are better together. They do not replace each other. LangChain is still really useful for building Large Language Model applications and higher-level things. LangGraph is better for organizing agents and their flows in a detailed way.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong><strong>Q4. Which one is better for production AI systems?<\/strong><\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">If your system is simple LangChain might be enough.. If your system needs to be strong, reliable and able to handle different paths and memories LangGraph is usually the better choice. In real-world production settings you might even use both LangChain and LangGraph together.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong><strong>Q5. How does this connect to AI careers?<\/strong><\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Knowing LangChain and LangGraph is important because companies want people who can actually build AI systems not just use existing tools. Understanding when to use LangChain and when to use LangGraph can help you design projects talk about architecture in job interviews and show that you have practical skills in AI engineering. This is what LangChain and LangGraph are all, about. They help you build Large Language Model applications and agent workflows.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong><strong>Final Summary<\/strong><\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">LangChain is the first framework for most learners because it gives a broad and practical way to start building LLM applications. This is really helpful for people who&#8217;re new to this. LangChain is a place to start. On the hand LangGraph is the better choice when you need to make sure things work together smoothly and you need to control many steps.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">If you are a student or you are working and you want to get into AI engineering the best way to learn is to start with LangChain and then add LangGraph later for advanced things like agent systems. This way of learning works with OneLeaps style of training which is practical and focuses on building real things rather than just learning theory. OneLeaps way is to build things that can be used in the world through actual projects.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For people who want to learn more about these skills in a way <a href=\"https:\/\/theoneleap.com\/\" rel=\"nofollow noopener\" target=\"_blank\">OneLeap<\/a> has a program called <a href=\"https:\/\/theoneleap.com\/programmes\/ai-engineering-mastery\" rel=\"nofollow noopener\" target=\"_blank\">AI Engineering Mastery<\/a>. This program covers topics like AI agents, agent frameworks and multi-agent systems. It also covers how to deploy things in production and how to work on projects that are like the ones you would find in the real world. The information in this article comes from LangChains documentation, LangGraphs page and AWSs guidance on agentic AI frameworks. LangChain and LangGraph are both important, for this.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Learn more about\u00a0<a href=\"https:\/\/vault.theoneleap.com\/index.php\/2026\/06\/14\/no-code-and-low-code\/\">No-Code and Low-Code vs Full Code Development: Which One Should You Choose?<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Stay updated with the latest AI, Data Science, and Automation insights by following&nbsp;<a href=\"https:\/\/theoneleap.com\/\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">OneLeap<\/a>&nbsp;on&nbsp;<a href=\"https:\/\/www.linkedin.com\/company\/theoneleap\/\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">LinkedIn<\/a>&nbsp;and&nbsp;<a href=\"https:\/\/www.instagram.com\/theoneleapofficial?igsh=MTk5MjltNGU0cTVyaA==\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">Instagram<\/a>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong><strong>Source links<\/strong><\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/docs.langchain.com\/oss\/python\/langchain\/overview\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">LangChain official overview<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.langchain.com\/langgraph\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">LangGraph official page<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/docs.aws.amazon.com\/prescriptive-guidance\/latest\/agentic-ai-frameworks\/langchain-langgraph.html\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">AWS Prescriptive Guidance: LangChain and LangGraph<\/a><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Introduction LangChain is a place to start for people who are new to this and for people who work. It helps you make LLM applications quickly. On the hand LangGraph is a good choice when you need to make things that can remember what they did before and that can do many things one, after [&hellip;]<\/p>\n","protected":false},"author":5,"featured_media":781,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_uag_custom_page_level_css":"","_swt_meta_header_display":false,"_swt_meta_footer_display":false,"_swt_meta_site_title_display":false,"_swt_meta_sticky_header":false,"_swt_meta_transparent_header":false,"footnotes":""},"categories":[99],"tags":[20,102,49],"class_list":["post-771","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-engineering","tag-ai-engineering","tag-langchain-vs-langgraph","tag-oneleap"],"spectra_custom_meta":{"_uagb_previous_block_counts":["a:90:{s:21:\"uagb\/advanced-heading\";i:0;s:15:\"uagb\/blockquote\";i:0;s:12:\"uagb\/buttons\";i:0;s:18:\"uagb\/buttons-child\";i:0;s:19:\"uagb\/call-to-action\";i:0;s:15:\"uagb\/cf7-styler\";i:0;s:11:\"uagb\/column\";i:0;s:12:\"uagb\/columns\";i:0;s:14:\"uagb\/container\";i:0;s:21:\"uagb\/content-timeline\";i:0;s:27:\"uagb\/content-timeline-child\";i:0;s:14:\"uagb\/countdown\";i:0;s:12:\"uagb\/counter\";i:0;s:8:\"uagb\/faq\";i:0;s:14:\"uagb\/faq-child\";i:0;s:10:\"uagb\/forms\";i:0;s:17:\"uagb\/forms-accept\";i:0;s:19:\"uagb\/forms-checkbox\";i:0;s:15:\"uagb\/forms-date\";i:0;s:16:\"uagb\/forms-email\";i:0;s:17:\"uagb\/forms-hidden\";i:0;s:15:\"uagb\/forms-name\";i:0;s:16:\"uagb\/forms-phone\";i:0;s:16:\"uagb\/forms-radio\";i:0;s:17:\"uagb\/forms-select\";i:0;s:19:\"uagb\/forms-textarea\";i:0;s:17:\"uagb\/forms-toggle\";i:0;s:14:\"uagb\/forms-url\";i:0;s:14:\"uagb\/gf-styler\";i:0;s:15:\"uagb\/google-map\";i:0;s:11:\"uagb\/how-to\";i:0;s:16:\"uagb\/how-to-step\";i:0;s:9:\"uagb\/icon\";i:0;s:14:\"uagb\/icon-list\";i:0;s:20:\"uagb\/icon-list-child\";i:0;s:10:\"uagb\/image\";i:0;s:18:\"uagb\/image-gallery\";i:0;s:13:\"uagb\/info-box\";i:0;s:18:\"uagb\/inline-notice\";i:0;s:11:\"uagb\/lottie\";i:0;s:21:\"uagb\/marketing-button\";i:0;s:10:\"uagb\/modal\";i:0;s:18:\"uagb\/popup-builder\";i:0;s:16:\"uagb\/post-button\";i:0;s:18:\"uagb\/post-carousel\";i:0;s:17:\"uagb\/post-excerpt\";i:0;s:14:\"uagb\/post-grid\";i:0;s:15:\"uagb\/post-image\";i:0;s:17:\"uagb\/post-masonry\";i:0;s:14:\"uagb\/post-meta\";i:0;s:18:\"uagb\/post-taxonomy\";i:0;s:18:\"uagb\/post-timeline\";i:0;s:15:\"uagb\/post-title\";i:0;s:20:\"uagb\/restaurant-menu\";i:0;s:26:\"uagb\/restaurant-menu-child\";i:0;s:11:\"uagb\/review\";i:0;s:12:\"uagb\/section\";i:0;s:14:\"uagb\/separator\";i:0;s:11:\"uagb\/slider\";i:0;s:17:\"uagb\/slider-child\";i:0;s:17:\"uagb\/social-share\";i:0;s:23:\"uagb\/social-share-child\";i:0;s:16:\"uagb\/star-rating\";i:0;s:23:\"uagb\/sure-cart-checkout\";i:0;s:22:\"uagb\/sure-cart-product\";i:0;s:15:\"uagb\/sure-forms\";i:0;s:22:\"uagb\/table-of-contents\";i:0;s:9:\"uagb\/tabs\";i:0;s:15:\"uagb\/tabs-child\";i:0;s:18:\"uagb\/taxonomy-list\";i:0;s:9:\"uagb\/team\";i:0;s:16:\"uagb\/testimonial\";i:0;s:14:\"uagb\/wp-search\";i:0;s:19:\"uagb\/instagram-feed\";i:0;s:10:\"uagb\/login\";i:0;s:17:\"uagb\/loop-builder\";i:0;s:18:\"uagb\/loop-category\";i:0;s:20:\"uagb\/loop-pagination\";i:0;s:15:\"uagb\/loop-reset\";i:0;s:16:\"uagb\/loop-search\";i:0;s:14:\"uagb\/loop-sort\";i:0;s:17:\"uagb\/loop-wrapper\";i:0;s:13:\"uagb\/register\";i:0;s:19:\"uagb\/register-email\";i:0;s:24:\"uagb\/register-first-name\";i:0;s:23:\"uagb\/register-last-name\";i:0;s:22:\"uagb\/register-password\";i:0;s:30:\"uagb\/register-reenter-password\";i:0;s:19:\"uagb\/register-terms\";i:0;s:22:\"uagb\/register-username\";i:0;}"],"_edit_lock":["1783614474:5"],"rank_math_internal_links_processed":["1"],"rank_math_primary_category":["99"],"rank_math_seo_score":["92"],"rank_math_focus_keyword":["LangChain"],"_thumbnail_id":["781"],"_uag_css_file_name":["uag-css-771.css"],"_uag_page_assets":["a:9:{s:3:\"css\";s:19131:\".wp-block-uagb-container{display:flex;position:relative;box-sizing:border-box;transition-property:box-shadow;transition-duration:.2s;transition-timing-function:ease}.wp-block-uagb-container 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