# LangChain Course by Eden Marco: Build AI Agents with LangChain and LangGraph > This is the code repository for a Udemy course on building AI agents with LangChain v1+ and LangGraph, structured around seven end-to-end projects from a hello-world chain to an agentic RAG system. Each project lives on a separate Git branch, and learners navigate by checking out branches and running scripts. **emarco177/langchain-course** — A project-based course repository for developing AI agents using LangChain v1+ and LangGraph: search agents, RAG systems, reflection agents, and code interpreters. - Repository: https://github.com/emarco177/langchain-course - Website: https://www.udemy.com/course/langchain/ - Stars: 1,695 · Forks: 3,229 - Language: Unknown - License: Apache-2.0 - Published: 2026-09-10 · Updated: 2026-09-10 · Language: en - Canonical page: https://hysenlabs.com/projects/emarco177-langchain-course ## Who This Course Is For and What It Expects The README explicitly states this is not a beginner course. Prerequisites include familiarity with Git, Python, environment variables, classes, testing, and debugging, plus Python 3.10 or later. The course explicitly excludes conda as a dependency manager, requiring instead uv, poetry, or pipenv. Machine learning experience is not required. The course targets developers who want to build real AI agent applications, not toy examples. The README describes it as built around "real projects using real APIs and real-world skills" and explicitly positions against courses with toy examples or filler content. LLM access is required. The README notes that learners can use open-source models via Ollama, or cloud providers such as OpenAI, Anthropic, or Gemini. This means all projects that call a language model will incur either API costs or local compute requirements. ## Seven Projects and the Branch-Per-Project Structure The repository uses a branch-per-project navigation model. The main branch contains only a README and a static/ directory. Each project lives on a named branch, and some projects are in external repositories linked from the README. The seven projects listed are: LangChain Hello World (basic structure and LLM integration), Modern Search Agent (LangChain v1's `create_agent` interface with Tavily integration and structured outputs), Agents Under The Hood (reasoning and acting patterns), RAG Gist (retrieval-augmented generation fundamentals), Documentation Helper (an external repo), Code Interpreter (AI-powered code execution), and Reflection/Reflexion/Agentic RAG agents hosted in a separate `langgraph-course` repository. The tech stack listed in the README includes LangChain v0.3+, LangGraph, Pinecone, FAISS, and Streamlit. The README frames the learning path in three phases: foundations (hello world and code interpreter), real-world applications (ice breaker and documentation helper), and advanced concepts (blog analyzer and agentic RAG). ## Setting Up and Running the First Project Clone the repository and navigate into it: ```bash git clone https://github.com/emarco177/langchain-course cd langchain-course ``` The README describes a branch-checkout workflow for each project. To start with the hello world project: ```bash git checkout project/hello-world uv sync uv run python main.py ``` To progress to a different project, check out its branch: ```bash git checkout project/code-interpreter uv sync uv run python main.py ``` For projects hosted in external repositories, the README says to clone the specific project repository and follow its own setup instructions. The Ice Breaker project, for example, is at github.com/emarco177/ice_breaker and requires its own setup. The README suggests following commits chronologically as a step-by-step learning method. Running `git log --oneline` shows the development progression within a branch. ## What Each Phase Teaches Phase 1, Foundations, covers the basic structure of a LangChain agent and how to add tool calling and code execution. The hello-world branch shows LLM integration and chain construction. The code interpreter branch adds external tool use and code execution as capabilities. Phase 2, Real-World Applications, moves to data collection and retrieval. The Ice Breaker project integrates social media data. The Documentation Helper implements RAG with a vector database for knowledge management. Phase 3, Advanced Concepts, covers multi-step reasoning with the Blog Analyzer and self-correcting agents with Agentic RAG. The README describes the agentic RAG project as an advanced retrieval-augmented generation system with self-correction. The course also covers RAG systems with vector databases, prompt engineering, agent workflows, deployment to production environments, error correction and self-improvement in agents, and optimising agent performance and cost efficiency. These are listed as learning objectives in the README rather than mapped to specific projects. ## Limitations: Separate Video Content and External Repositories The repository contains only code, not the video lectures. Enrolling in the Udemy course at udemy.com/course/langchain/ is required to access the video explanations and a structured learning sequence. The repository alone without the lectures is a set of code samples without the teaching context. Several of the most advanced projects (Documentation Helper, Reflection Agent, Reflexion Agent, Agentic RAG) are in external repositories. The langchain-course repository references these with links but does not contain their code. A learner who wants to follow the full seven-project sequence will need to clone multiple repositories. The README also states that the 2026 cohort uses `uv` and explicitly says conda is not supported. Learners with conda-based environments will need to set up a separate uv or pipenv environment. ## Comparison With LangChain Academy and DeepLearning.ai LangChain Academy is the official free learning resource from the LangChain team, offering structured tracks on building applications with LangChain and LangGraph. The Academy content is kept current with library changes because it is maintained by the library authors. DeepLearning.ai also offers short courses on LangChain, some developed in partnership with LangChain. These are typically single-session, self-contained, and focused on specific topics rather than end-to-end project construction. This repository differs in its project-based structure: rather than isolated demonstrations, it builds complete applications. The branch-per-project layout means each project shows a full working implementation from setup to output. The trade-off is that the repository requires Udemy access to get the explanation layer that the official Academy and DeepLearning.ai provide for free. ## Maintenance and License The repository is not archived. The last push was on 2026-09-02. The README contains a July 2026 coupon code link for the Udemy course, indicating recent updates. The project is licensed under Apache-2.0. The course was built by Eden Marco, who links a Twitter account and LinkedIn profile in the README. The repository is a companion to a paid Udemy course, so code branches are maintained in step with course updates and new LangChain versions. The explicit targeting of LangChain v1.0+ suggests the course material was revised when LangChain released its v1 API, which introduced the `create_agent` interface used in the search agent project. ## Conclusion This repository is the right starting point for software engineers with basic Python and Git experience who want to learn LangChain and LangGraph through complete, working projects rather than tutorials. The Udemy video lectures are separate from this repository and require enrollment. Developers who want a free, text-based LangChain reference should check the official LangChain documentation and LangChain Academy. ## FAQ ### Is LangChain difficult to learn? The course README states this is not a beginner course and expects familiarity with Python, Git, environment variables, and basic debugging. No machine learning experience is required, but LangChain's abstractions over prompting, tool calling, and agent loops have a learning curve that rewards prior Python experience. ### How long will it take to learn LangChain? The README describes seven end-to-end projects across three phases, from a basic hello-world chain to advanced agentic RAG. The course does not specify a duration; the pace depends on prior Python experience and how deeply learners explore each project branch. ### What is the best course for learning LangChain? This repository accompanies a Udemy course by Eden Marco that covers seven end-to-end projects with LangChain v1+ and LangGraph. LangChain Academy, maintained by the LangChain team, is the official free alternative and stays current with library updates. ### How to learn LangChain? This repository suggests following the commits chronologically within each project branch using `git log --oneline`, and running `uv sync` followed by `uv run python main.py` at each stage. The README also recommends reading the official LangGraph documentation and tutorials. ## Sources - [emarco177/langchain-course on GitHub](https://github.com/emarco177/langchain-course) - [Issues](https://github.com/emarco177/langchain-course/issues) - [License: Apache-2.0](https://github.com/emarco177/langchain-course/blob/main/LICENSE) - [Project website](https://www.udemy.com/course/langchain/) - [README](https://github.com/emarco177/langchain-course/blob/main/README.md) --- Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/emarco177-langchain-course