LangChain- Agentic AI Engineering with LangChain & LangGraph (Udemy.com)

I really liked this course because it covers exactly what I needed. I think Eden Marco is doing his best to keep the content up to date. Thank you, Eden! While some sections may feel a bit mixed because newer content is integrated into earlier lessons, the code and concepts remain relevant and easy to follow. The explanations are detailed, and the way he walks through the overall flow helped me build a solid and comprehensive understanding of the underlying technology.

I'm halfway through the course and so far the course has been excellent. People who are badmouthing this course saying the development environment, videos, etc. are not consistent and that the course doesn't have any structure are simply nitpickers. Since the AI/LLM landscape is changing every single day, and the LangChain framework is evolving at a rapid pace, Eden is doing a commendable job at coming back and re-recording portions with new content. This course is simply amazing, would recommend this to everyone. If you want to get practical knowledge, don't think twice, just take this course.

I absolutely love the hands-on approach of this course—it covers a massive breadth of topics while keeping you actively building throughout. To make the workflow even smoother for future students, I have two quick suggestions: Pacing during code demos: When demonstrating syntax (especially for LangChain), typing out the code manually rather than relying on IDE auto-complete would make it much easier to follow the logic and grasp the patterns step-by-step. Currently, most commit links point to the finished code for an entire section, which can make tracking incremental progress tricky. However, the recent update to the Agentic RAG section—where lesson-by-lesson commits were introduced—was phenomenal! Rolling out that same recent update across the rest of the course curriculum would make following along seamless everywhere.

This is really an amazing, technical and inspiring course about AI engineering, langchain / langgraph and agentic application. Before starting, I was purely expecting the very technical skill on how to build up a modern AI application, learning their tech stack and structure, but what really cool is that on top of those, Eden actually also goes through the evolution with us, the prompt-to-agent evolution, the new software engineering appearance and agentic era, nowadays the FOMO psychology, and many more bonus mental mindset stuff, which gives great comprehensiveness and really makes us feel the "crystal clear" about AI, not only the skill, and really something making this course an excellent one. I enjoyed the course and would definitely like to recommend this course to you.

course positive sides: Good for beginners, as it describes the concepts very clearly Repetitions, the trainer recall and shortly explains the concepts everytime it talks about it. Not only said once in Video 1 and never give a clue in video n. However this makes the content longer and sometime unnecessary. The course tries to be update to date with the technologies and some videos are recorded again to cover the tech updates. Course negative sides: Many projects examples are taught, but they miss the match to a real world example uses cases. This would explain a beginner to map the example to a real world use case or examples out there. The projects codes run and are good examples, but they miss how that example can be adopted into a production system. E.g. how they can be integrated with an API to be a real service and be ready and deployable service. and to my own expectations, not very advanced. I expected deployable, standard interfaced example projects that had everything along with that core concept of the course.

The course covered all the topics I was looking for. I have understood what agents are and how they work internally. The introductory concepts to Langgraph and MCP very helpful in understanding the need for them and the problems they solve. Overall, it's a solid course which gave me a complete picture of Agentic AI. One suggestion I'd like to provide is to establish the agenda of the sections more clearly. For example, it wasn't quite clear why the sections related to Reflection Agent and Reflexion Agent were present until the sections were complete.

The course is great but has 3 issues 1- No LCEL implementations and types and examples and this is one of core components of langchain that should have a dedicated section for it ( i didnt see much of LCEL till now im in sec 11) 2- its very good to know old versions implementations but come on 3 sections for it( layer 1 -2 -3) , it can be all compacted to one section and give the rest of the effort to make new implantations and projects 3- course with this size should contain more E2E projects not only examples at last the course is very useful but lacks these 3 important things and it will be the best langchain course ive ever took

Just wanted to share some feedback on this course — it's genuinely one of the most academically led AI courses I've come across. The way it stays grounded in the original research is something I really appreciate. What stood out most was your implementation of Self-RAG and Adaptive-RAG, May be I should say one of stood outs. The Adaptive-RAG translation in particular is brilliant — you've essentially rendered what reads in the paper as an SLM-based routing mechanism as a LangChain chain, and it works elegantly. That kind of creative bridging between theory and practical implementation is rare. I did not finish all the way to the end yet. But thanks for putting this together. It's clear a lot of thought went into it.

This instructor is truly exceptional he does not just teach Generative AI, LangChain, MCP, and agents at a surface level. He explains the history, the reasoning, and the evolution behind the tools. For example, when teaching agent loops, he guides you from LangChain abstractions back to vanilla OpenAI implementations and earlier concepts such as ReAct style agents. That makes the material much deeper and easier to remember. His READMEs, diagrams, examples, Python coding style, and the free/third-party tools he introduces are also excellent. You learn not only AI concepts, but also how to structure projects and think like an AI engineer. He is clearly passionate, talented, and serious about teaching. His courses make complex topics practical, memorable, and genuinely enjoyable. Highly recommended.

I have just finished this course and honestly it was the best practical courses I have taken. What I liked most was the way concepts were taught interactively with actual coding implementations and product building instead of only theory. I also really liked that the course covered security, production best practices, and real-world development approaches. Another big plus is that the course keeps getting updated whenever new LangChain or LangGraph concepts are introduced, which makes it feel like a long-term one stop resource for learning these frameworks. Learned a lot from this course. Kudos to Eden for the immense effort and passion put into creating and maintaining it.