Every serious AI developer in 2026 keeps bumping into the same two names: LangChain and LangGraph. And for good reason. ZipRecruiter data from February 2026 puts the average LangChain developer salary at $109,905 per year in the US, with top earners crossing $169,500. More telling: Glassdoor currently lists hundreds of open remote LangChain and LangGraph roles — and nearly every job posting mentions RAG, AI agents, and vector databases in the same breath.
The hard part isn’t finding a course. The hard part is finding one worth your time. We’ve done the filtering for you — reviewing the full Udemy catalog this February 2026 and selecting 10 courses that are genuinely up-to-date, project-driven, and built for developers who want to ship real AI systems, not just understand the theory.
Whether you’re building your first RAG pipeline or designing production-ready multi-agent architectures, there’s a course on this list for exactly where you are right now. Check our Udemy Coupon Code page for the latest deals before you buy.
Why LangChain, LangGraph & RAG Are the Skills to Learn in 2026
LangChain is the framework that connects large language models to real-world data sources, tools, APIs, and memory systems. Without it, building anything beyond a basic chatbot becomes an exercise in reinventing infrastructure. With it, developers can wire up GPT-4, Claude, Gemini, or any open-source model into sophisticated applications in a fraction of the time.
LangGraph takes that further. It’s the orchestration layer that lets you build stateful, multi-step AI workflows — the kind where agents reason, plan, loop back on themselves, and collaborate with other agents. In 2026, LangGraph has become the go-to tool for building agentic AI that actually works in production.
Retrieval-Augmented Generation (RAG), meanwhile, is how you make LLMs useful with your own data. Instead of hallucinating answers, a well-built RAG system retrieves relevant documents from a vector database and grounds the model’s responses in real information. Traditional RAG is table stakes now — the industry has moved to advanced RAG patterns like hybrid search, corrective RAG, self-RAG, and agentic RAG.
Together, these three technologies form the backbone of modern AI application development. If you’re serious about working in AI engineering, building GenAI products, or transitioning into an LLM-focused role, this is exactly where to invest your learning time.
How We Selected These Courses
Every course on this list was reviewed and verified in February 2026 against these criteria:
- Rating: ≥ 4.5 stars from verified student reviews
- Enrollment: Meaningful student adoption (from emerging hot-and-new to 150k+ enrolled)
- Freshness: Updated to cover LangChain v1.0+, LangGraph latest, and current RAG patterns
- Hands-on depth: Real projects, not slide decks — functional AI apps you can actually deploy
- Instructor credibility: Verified industry experience or large verified teaching track record
- Coverage: At least one of LangChain, LangGraph, RAG, or AI Agents as a primary focus
Ten courses met all criteria. Here’s the full breakdown.
10 Udemy Courses to Master LangChain, LangGraph & RAG in 2026
1. Ultimate RAG Bootcamp Using LangChain, LangGraph & LangSmith — Krish Naik
Best for: The most comprehensive RAG course on Udemy — from traditional pipelines to agentic multi-agent RAG.
If there’s one RAG course that deserves to be called definitive, this is it. Krish Naik — one of the most respected ML educators globally — built this bootcamp to take you through the complete evolution of RAG: from simple retrieval pipelines all the way to autonomous, multi-agent RAG systems that can reason, self-correct, and collaborate.
You’ll build with LangChain, LangGraph, and LangSmith as a unified stack. That means you’re not just learning how to retrieve documents — you’re learning how to track experiments, debug retrieval bottlenecks, optimize performance with LangSmith, and deploy pipelines that hold up under real workloads. The coverage of vector databases alone (FAISS, Pinecone, Weaviate) is deeper than most standalone courses on the topic.
The multimodal RAG section is a particular standout. Building AI assistants that process both text and images in a single retrieval pipeline is genuinely advanced territory — and Krish covers it with the same clarity he brings to everything else.
What you’ll learn:
- Traditional RAG pipelines: chunking strategies, embeddings, vector retrieval
- Advanced retrieval: hybrid search (vector + keyword), multimodal RAG, persistent memory
- Self-RAG, Adaptive RAG, and Corrective RAG for production-grade accuracy
- Multi-agent RAG architectures with LangGraph for collaborative AI reasoning
- LangSmith for experiment tracking, debugging, and pipeline optimization
- Vector databases: FAISS, Pinecone, and Weaviate in depth
- Real-world projects: domain-specific chatbots, multi-agent research assistants, multimodal AI tools
Who this is for: AI beginners and experienced developers who want end-to-end RAG mastery; anyone building production AI systems with LangChain.
Enrollment: 18,964 students | Rating: 4.6/5 | Duration: 31 hours | Badge: 🏆 Best Seller
→ Get Ultimate RAG Bootcamp on Udemy
2. LangGraph — Develop LLM Powered AI Agents with LangGraph — Eden Marco
Best for: Developers who want to move fast building real-world LangGraph agents — without the bloat.
Eden Marco has a reputation for courses that get straight to the point and deliver real skills fast. This LangGraph course lives up to that reputation. At 7.5 hours, it’s one of the most focused options on this list — every hour is spent building actual AI agents in Python, not sitting through conceptual setup.
You’ll learn LangGraph’s state machine architecture, understand how nodes, edges, and conditional routing work together, and build real-world agents with memory, tool-calling, and multi-step reasoning. By the time you finish, you’ll have the mental model and hands-on experience to build LangGraph agents from scratch on your own terms.
If you’re already comfortable with Python and want to add LangGraph to your toolkit quickly, this is the most efficient path on this entire list.
What you’ll learn:
- LangGraph state machines: nodes, edges, conditional routing, and agent loops
- Building LLM agents with persistent memory and tool-calling capabilities
- Real-world AI agent patterns using Python and LangGraph
- ReAct agent loops and reasoning workflows
- Fast, practical implementation from day one
Who this is for: Software developers and AI engineers who want to learn LangGraph quickly through real projects; intermediate Python developers entering AI agent development.
Enrollment: 23,990 students | Rating: 4.5/5 | Duration: 7.5 hours | Badge: 🏆 Best Seller
→ Get LangGraph AI Agents on Udemy
3. LangChain — Develop AI Agents with LangChain & LangGraph — Eden Marco
Best for: Software engineers and data scientists who want the most thorough, production-focused LangChain + LangGraph course available.
With 156,764 students enrolled, this is the single most popular LangChain course on Udemy — and it earns that status. Eden Marco re-recorded the entire course to support LangChain v1.0+, so you’re learning the current API, not a version that’s already been deprecated. Everything is built around real projects: no toy examples, no slides-only theory.
The course builds a Documentation Helper chatbot with advanced RAG, a Code Interpreter assistant, and multiple agent implementations using ReAct, Chain of Thought, and Few-Shot prompting. The deep dive into how LangChain is structured under the hood — including manual JSON schemas vs. tool abstractions — gives you the kind of understanding that separates developers who can debug their own agents from those who can only copy tutorial code.
This isn’t a beginner course. It assumes Python proficiency and software engineering fundamentals. But if you meet that bar, it’s the most thorough LangChain investment you can make right now. For a broader tour of the AI course landscape, browse Topics on CoursesWyn.
What you’ll learn:
- LangChain v1.0+ fundamentals and advanced patterns
- Prompt engineering: Chain of Thought, ReAct, Few-Shot, and prompt theory
- Building RAG systems with advanced retrieval — vector databases, embeddings, document loaders
- LangGraph integration for stateful, multi-step agent workflows
- Real project: Documentation Helper chatbot with production-grade RAG
- Real project: Slim ChatGPT Code Interpreter
- Understanding LangChain’s internal architecture (not just its API surface)
Who this is for: Software engineers, data scientists, and AI/ML engineers with Python background; anyone targeting AI Engineer or Generative AI Developer roles.
Enrollment: 156,764 students | Rating: 4.6/5 | Duration: 20.5 hours | Badge: 🏆 Best Seller
→ Get LangChain + LangGraph AI Agents on Udemy
4. RAG for Professionals with LangGraph, Python and OpenAI — Alexander Hagmann
Best for: Business-focused developers building production RAG systems over internal enterprise documents.
Most RAG courses teach you how to build systems that work on public data. This course teaches you how to build RAG systems that work on sensitive internal business documents — which is what most real enterprise use cases actually require. Alexander Hagmann frames the entire curriculum around production deployment from day one, using LangChain, LangGraph, OpenAI, ChromaDB, and Python as a coherent stack.
With a 4.8 rating and 144 lectures packed into 11 hours, this is a remarkably dense and well-reviewed course. The emphasis on Chroma for document storage, proper chunking strategies for business PDFs and internal knowledge bases, and OpenAI API integration makes it one of the most practically grounded RAG courses available. If you’re building AI for a company rather than a side project, this is the one to take.
What you’ll learn:
- Production-ready RAG architecture for internal business document systems
- LangChain and LangGraph for building robust enterprise AI pipelines
- ChromaDB for vector storage and efficient document retrieval
- OpenAI API integration with proper prompt and retrieval design
- Security-conscious design for enterprise AI deployments
- End-to-end AI system deployment from development to production
Who this is for: Developers and engineers building AI systems for business use cases; anyone deploying RAG over internal documents, PDFs, or enterprise knowledge bases.
Enrollment: 352 students | Rating: 4.8/5 | Duration: 11 hours | Badge: 🏆 Best Seller
→ Get RAG for Professionals on Udemy
5. 2026 Deep Agent — Multi-Agent RAG with Gemini and LangChain — KGP Talkie
Best for: Developers ready to go deep on multi-agent RAG, Google Gemini 3, and advanced agentic AI patterns.
KGP Talkie — the teaching brand of Laxmi Kant Tiwari, an IIT Kharagpur graduate with 10+ years of industry experience and 100,000+ students trained worldwide — brings one of the freshest courses on this list. Released in 2026 and marked Hot & New, this is a course built entirely around where the industry is heading: multi-agent RAG systems powered by Google Gemini 3 and LangChain v1.
The combination of Qdrant as a vector database, Docker for containerized deployment, Docling for document processing, and Gemini 3 integration makes this course genuinely forward-looking. You won’t find this specific stack in any other course on Udemy right now. If you want to be building with the tools that enterprise AI teams will be using in the next 12 months, this is the course to take.
What you’ll learn:
- LangChain v1 AI agents and multi-modal deep agents from scratch
- Multi-agent Deep Advanced RAG architectures
- Google Gemini 3 integration with LangChain pipelines
- Qdrant vector database for scalable document retrieval
- Docker for containerized AI deployment
- Docling for advanced document processing and ingestion
Who this is for: Intermediate to advanced developers who want to build with Google Gemini and next-generation multi-agent RAG; engineers keeping pace with the 2026 AI stack.
Enrollment: 1,776 students | Rating: 4.7/5 | Duration: 20 hours | Badge: 🔥 Hot & New
→ Get 2026 Deep Agent on Udemy
6. Master LangChain v1 and Ollama — Chatbot, RAG and AI Agents — KGP Talkie
Best for: Developers who want to build and deploy LangChain apps with local LLMs using Ollama — no cloud API costs required.
Running LLMs locally is no longer a niche skill — it’s a serious cost and privacy strategy that more development teams are adopting in 2026. This course covers the full LangChain + Ollama stack, teaching you how to deploy LangChain v1 applications with local models like DeepSeek, LLAMA, Qwen3, and Gemma3, as well as cloud deployments on AWS.
The Text-to-MySQL feature is a particularly practical addition — letting you build natural language interfaces over relational databases is one of the most requested enterprise AI use cases right now. With 17.5 hours of content and 171 lectures, this is one of the more comprehensive Ollama + LangChain courses you’ll find anywhere.
What you’ll learn:
- LangChain v1 with local LLMs via Ollama: DeepSeek, LLAMA, Qwen3, Gemma3, GPT-OSS
- Chatbot, RAG system, and AI agent development from scratch
- AWS deployment for LangChain AI applications
- Text-to-MySQL for natural language database query interfaces
- Local AI development workflow — zero cloud API dependency
- Full pipeline from local development to cloud deployment
Who this is for: Developers wanting to run LLMs locally without OpenAI costs; engineers building privacy-conscious AI apps; anyone interested in the open-source LLM ecosystem.
Enrollment: 6,044 students | Rating: 4.6/5 | Duration: 17.5 hours | Badge: 🏆 Best Seller
→ Get Master LangChain v1 and Ollama on Udemy
7. LangChain Framework for Beginners — Build AI Systems + RAG — Rahul Shetty Academy
Best for: TypeScript developers and beginners who want to learn LangChain 1.0 with a fresh, modern curriculum including MCP integration.
Most LangChain courses are Python-only. This one covers LangChain 1.0 with TypeScript — which is a meaningful gap to fill, since a huge share of full-stack and frontend developers work primarily in JavaScript/TypeScript and want to integrate LLMs without switching languages.
Rahul Shetty Academy also brings something few other courses on this list include: Model Context Protocol (MCP) integration. MCP is becoming an increasingly important standard for how AI agents connect to external tools and services, and seeing it covered here alongside AI Agents, RAG Pipelines, Agentic RAG, and LangGraph Deployment makes this one of the most forward-thinking beginner courses available. At just 6.5 hours, it’s also one of the most efficient ways to get up to speed.
What you’ll learn:
- LangChain 1.0 with TypeScript — not just Python
- AI Agents and tool integration from the ground up
- RAG pipelines and Agentic RAG implementation
- Model Context Protocol (MCP) integration with LangChain
- LangGraph deployment patterns
- Practical AI system building from a beginner-friendly starting point
Who this is for: TypeScript developers, full-stack engineers, and beginners wanting a modern LangChain entry point; developers interested in MCP-based AI architectures.
Enrollment: 1,247 students | Rating: 4.7/5 | Duration: 6.5 hours | Badge: 🏆 Best Seller
→ Get LangChain Framework for Beginners on Udemy
8. OpenClaw Agents, LangChain, HuggingFace, LLM & Gen AI — Ankit Mistry
Best for: Developers who want the widest model coverage — OpenAI, Gemini, DeepSeek R1, Azure AI, and HuggingFace in one course.
If you’ve been frustrated by courses that lock you into a single LLM provider, this one solves that problem. Ankit Mistry’s course covers an unusually wide range of models and tools: OpenAI, Gemini, DeepSeek R1, NotebookLM, Azure AI, and HuggingFace — all integrated with LangChain and OpenClaw Agents in a single curriculum.
With 9,712 students and 157 lectures spread across 20.5 hours, this is one of the most content-rich courses on the list. It’s a Best Seller with a 4.6 rating and strong student numbers, which reflects its real-world usefulness: developers who need to work across multiple LLM providers rather than being tied to a single API find this course’s breadth genuinely valuable. Check the Courses page for the latest coupon pricing on this and similar GenAI courses.
What you’ll learn:
- OpenClaw Agents architecture and implementation
- LangChain integration with OpenAI, Gemini, DeepSeek R1, and Azure AI
- HuggingFace model integration with LangChain pipelines
- Generative AI and LLM fundamentals across multiple providers
- NotebookLM for document-based AI workflows
- Multi-provider LLM applications built with Python
Who this is for: Developers working across multiple LLM providers; engineers building flexible AI applications that aren’t locked into a single API; anyone wanting broad GenAI + LangChain coverage.
Enrollment: 9,712 students | Rating: 4.6/5 | Duration: 20.5 hours | Badge: 🏆 Best Seller
→ Get OpenClaw Agents & LangChain on Udemy
9. AI-Agents: Automation & Business with LangChain & LLM Apps — Arnold Oberleiter
Best for: Entrepreneurs and developers who want to build AI-powered business automation and learn how to monetize LangChain applications.
Most LangChain courses focus purely on the technical side. Arnold Oberleiter’s course takes a different angle — it teaches you how to build AI agents and LLM applications with a business and monetization mindset. With 26,044 students and a 4.5 rating, it’s clearly resonating with the developer-entrepreneur audience.
The course covers Node.js and JavaScript alongside Python, making it one of the few multilingual LangChain courses available. Topics include business task automation, how to sell AI software products, and the LangGraph integration needed for building agentic workflows at scale. If you’re building AI for clients or want to productize your LangChain skills, this course has the clearest path from technical skills to commercial outcomes.
What you’ll learn:
- AI agents for business automation with LangChain and LangGraph
- Node.js and JavaScript LangChain implementations (alongside Python)
- Building LLM apps with GPT, Claude, and RAG systems
- How to automate client tasks and build sellable AI software products
- Multi-provider LLM integration for commercial applications
- Path from development to monetization of AI agent products
Who this is for: Developer-entrepreneurs, freelancers building AI products for clients, full-stack developers (JavaScript/Node.js) entering AI development, anyone interested in the commercial side of LangChain.
Enrollment: 26,044 students | Rating: 4.5/5 | Duration: 10 hours | Badge: 🏆 Best Seller
→ Get AI-Agents Automation & Business on Udemy
10. LangGraph Mastery: Develop LLM Agents with LangGraph — Andrei Dumitrescu
Best for: A structured, systematic introduction to LangGraph for developers who prefer a well-organized, concept-first approach.
Andrei Dumitrescu is a consistently well-reviewed Udemy instructor known for structured, methodical teaching — and this LangGraph course reflects that style. At 5.5 hours, it’s one of the shorter options on this list, but what it lacks in length it makes up for in conceptual clarity. This is the course to take if you want to genuinely understand how LangGraph works before writing a line of agent code.
Topics cover LangChain and LangGraph integration, AI workflow design, task automation with LLM agents, and building applications that transform how work gets done. The 4.5 rating with 258 reviews indicates strong and consistent student satisfaction — particularly from developers who found other LangGraph courses too fast-paced or too abstract.
What you’ll learn:
- LangGraph fundamentals: state, nodes, edges, conditional logic, and memory
- Building LLM-powered AI workflows and automation systems
- LangChain + LangGraph integration patterns
- Transforming applications with agent-based AI reasoning
- Structured approach to designing reliable agentic systems
Who this is for: Developers who prefer a structured, concept-first approach to learning LangGraph; anyone who found other agent courses too fast or too abstract.
Enrollment: 3,583 students | Rating: 4.5/5 | Duration: 5.5 hours | Badge: 🏆 Best Seller
→ Get LangGraph Mastery on Udemy
Quick Comparison: All 10 Courses at a Glance
| # | Course | Instructor | Students | Rating | Hours | Key Strength | Badge |
|---|---|---|---|---|---|---|---|
| 1 | Ultimate RAG Bootcamp | Krish Naik | 18,964 | 4.6 | 31 | Complete RAG mastery | 🏆 Best Seller |
| 2 | LangGraph AI Agents | Eden Marco | 23,990 | 4.5 | 7.5 | Fast LangGraph skills | 🏆 Best Seller |
| 3 | LangChain + LangGraph Agents | Eden Marco | 156,764 | 4.6 | 20.5 | Most popular, production-grade | 🏆 Best Seller |
| 4 | RAG for Professionals | Alexander Hagmann | 352 | 4.8 | 11 | Enterprise RAG deployment | 🏆 Best Seller |
| 5 | 2026 Deep Agent | KGP Talkie | 1,776 | 4.7 | 20 | Multi-agent + Gemini 3 | 🔥 Hot & New |
| 6 | LangChain v1 + Ollama | KGP Talkie | 6,044 | 4.6 | 17.5 | Local LLM development | 🏆 Best Seller |
| 7 | LangChain for Beginners (TS) | Rahul Shetty Academy | 1,247 | 4.7 | 6.5 | TypeScript + MCP | 🏆 Best Seller |
| 8 | OpenClaw Agents + LangChain | Ankit Mistry | 9,712 | 4.6 | 20.5 | Multi-provider LLM coverage | 🏆 Best Seller |
| 9 | AI-Agents Business Automation | Arnold Oberleiter | 26,044 | 4.5 | 10 | Business + monetization focus | 🏆 Best Seller |
| 10 | LangGraph Mastery | Andrei Dumitrescu | 3,583 | 4.5 | 5.5 | Structured LangGraph intro | 🏆 Best Seller |
How to Choose the Right Course for You
You’re new to LangChain and want to start with the fundamentals → #7 LangChain Framework for Beginners is the cleanest entry point. Short, modern, TypeScript-friendly, and covers MCP — the stack you’ll actually use in 2026.
You want the most popular, battle-tested LangChain course on Udemy → #3 LangChain + LangGraph Agents by Eden Marco. 156k+ students don’t lie — this is the one most working AI engineers have gone through.
You want deep RAG knowledge, start to finish → #1 Ultimate RAG Bootcamp by Krish Naik. From basic retrieval to agentic multi-agent RAG at 31 hours, nothing else comes close in scope.
You’re building for enterprise or business clients → #4 RAG for Professionals (highest rating on the list at 4.8) for internal document systems, or #9 AI-Agents Business Automation for the full commercial angle.
You want the freshest 2026 tooling (Gemini 3, Qdrant, multi-agent) → #5 2026 Deep Agent by KGP Talkie. Brand new, hot and new badge, and built around tools others haven’t caught up to yet.
You want to run LLMs locally without cloud API bills → #6 Master LangChain v1 and Ollama. Local LLMs with LangChain, AWS deployment, and Text-to-MySQL — practical and cost-effective.
You need multilingual coverage (JavaScript/Node.js + Python) → #9 AI-Agents Business Automation by Arnold Oberleiter covers both languages in a single course.
You prefer a slow, structured LangGraph introduction → #10 LangGraph Mastery by Andrei Dumitrescu — methodical, clear, concept-first before code.
Frequently Asked Questions
Do I need prior AI experience to take these LangChain courses?
It depends on the course. #7 LangChain Framework for Beginners is genuinely beginner-accessible. #3 LangChain + LangGraph by Eden Marco and #4 RAG for Professionals assume Python and software engineering basics. #5 2026 Deep Agent and #1 Ultimate RAG Bootcamp are best after you have some LLM or Python foundations in place.
Are these courses updated for LangChain v1.0 and LangGraph latest?
Yes — all ten courses were verified in February 2026. Courses #2, #3, #6, and #7 explicitly mention LangChain v1.0+ and LangGraph current releases. Course #5 (2026 Deep Agent) is brand new and built entirely on the current stack.
What’s the difference between LangChain and LangGraph?
LangChain is the framework for building LLM-powered applications: it handles model connections, prompt templates, memory, tools, and RAG chains. LangGraph is built on top of LangChain and adds a state machine layer for building stateful, multi-step agent workflows — the kind where an AI agent reasons through multiple steps, branches conditionally, loops back on its results, and coordinates with other agents. In 2026, most production AI systems use both together.
What is RAG and why is it important?
RAG (Retrieval-Augmented Generation) is the technique of connecting an LLM to an external knowledge source — a vector database, document store, or knowledge base — so it can answer questions grounded in your specific data rather than hallucinating from training memory. It’s the difference between a generic chatbot and an AI assistant that actually knows your company’s documents, codebase, or product catalog.
How much do these courses cost on Udemy?
Udemy’s regular sales bring most courses down to under $20. You can find the latest verified discounts for all these courses on our Udemy Coupon Code page.
Is learning LangChain worth it for my career in 2026?
Absolutely. ZipRecruiter data shows LangChain developers earning an average of $109,905 per year, with top earners reaching $169,500. More importantly, LangChain and LangGraph skills appear in job postings across AI engineering, backend development, data science, and even full-stack roles — it’s a genuinely cross-functional skill that’s becoming a baseline expectation for anyone building AI applications.
Conclusion
LangChain, LangGraph, and RAG have moved from “interesting experiments” to “production infrastructure” in the span of two years. In 2026, these aren’t optional skills for AI developers — they’re the foundation everything else is built on.
The ten courses on this list cover every angle: from beginner introductions to advanced multi-agent systems, from local Ollama deployments to enterprise-grade RAG architectures, from Python-first curricula to TypeScript-native approaches. All are verified as of February 2026, all emphasize real projects over theory, and all represent instructors who have genuinely done this work.
Pick the one that matches your current level and your target outcome. Start building. The AI application revolution is happening right now — and these courses put the tools directly in your hands.
All courses are available through the links above, typically for under $20 during Udemy sales.
Affiliate disclosure: CoursesWyn uses affiliate links. We may earn a small commission at no extra cost to you when you purchase through our links. Our course recommendations are based on genuine evaluation — commissions never influence rankings or which courses we include.
📚 Related Course Recommendation
Based on this article, you might be interested in this highly-rated course
