LangChain – Develop AI Agents with LangChain & LangGraph | Course Overview & Key Highlights (2025)
Course introduction
LangChain – Develop AI Agents with LangChain & LangGraph, created by Eden Marco, is an advanced, hands-on AI engineering course focused on building real-world AI agents using LangChain v1.0+ and LangGraph. Updated in December 2025, this course is designed for developers who want to go beyond basic prompting and build production-grade agentic AI systems.
The course emphasizes end-to-end AI agent development, covering prompt engineering, RAG pipelines, vector databases, context engineering, and modern agent orchestration using LangGraph.
Course details
- Instructor: Eden Marco
- Last updated: December 2025
- Language: English
- Duration: ~18 hours (18h 3m total)
- Rating: ★4.6 / 5
- Learners: 144,737+
- Price (typical sale): ~US$7–12 (varies by Udemy promotions)
- Access: Lifetime (mobile + TV)
- Certificate: Udemy Certificate of Completion included
Key highlights
- Fully updated for LangChain 1.0+ (re-recorded course)
- Build end-to-end generative AI agents
- Deep dive into prompt engineering (Chain-of-Thought, ReAct, Few-Shot)
- Learn Context Engineering and memory strategies
- Hands-on with RAG (Retrieval-Augmented Generation) pipelines
- Vector databases: Pinecone, FAISS
- Agent orchestration using LangGraph
- Understand LangChain’s internal architecture & codebase
- Model Context Protocol (MCP) explained
- Designed for software engineers, AI engineers, and data scientists
What you will learn
LangChain Fundamentals
- Core LangChain concepts and architecture
- Chains, agents, tools, memory, and output parsers
- Document loaders and text splitters
Prompt & Context Engineering
- Chain-of-Thought prompting
- ReAct prompting strategies
- Few-shot prompting techniques
- Designing effective context windows
AI Agents & Agentic Workflows
- Designing autonomous AI agents
- Tool-using agents and decision loops
- Multi-step reasoning and planning
RAG & Vector Databases
- Build Retrieval-Augmented Generation systems
- Vector stores with Pinecone & FAISS
- Embeddings, indexing, and retrieval strategies
LangGraph
- Graph-based agent orchestration
- Stateful, multi-agent workflows
- Controlling execution paths and agent memory
LLM Theory for Engineers
- Large Language Model fundamentals
- Practical understanding for software developers
- Using APIs to connect with foundation models
Production-Oriented Skills
- Navigating the LangChain open-source codebase
- Debugging, testing, and structuring AI systems
- Building scalable, maintainable AI applications
Frequently asked questions (FAQ)
Q — Is this course beginner-friendly?
A — No. This is an advanced course. Basic software engineering knowledge (Python, Git, environments) is required.
Q — Do I need machine learning experience?
A — No prior ML experience is required. The focus is on AI engineering, not model training.
Q — Does the course cover the latest LangChain version?
A — Yes. The course is fully re-recorded and supports LangChain v1.0+.
Q — Will I build real AI agents?
A — Yes. You will build complete, working agentic AI systems end to end.
Q — Is a certificate included?
A — Yes. Udemy provides a Certificate of Completion.
Why this course is worth it
This course stands out because it goes beyond tutorials and teaches how modern AI agents actually work under the hood. It’s ideal for developers who want to build serious LLM-powered systems, understand LangChain internals, and design scalable agent architectures using LangGraph.
Final verdict
If you already know Python and software engineering basics and want to move into AI agent development, this course is a strong, practical choice. It focuses on real engineering problems—not hype—and gives you the tools to build production-ready AI agents with LangChain and LangGraph.
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