LangChain4j for Beginners: A Microsoft Course for Building AI Applications in Java

What the Course Covers and Who It Is For

LangChain4j for Beginners is a structured learning repository published by Microsoft that teaches Java developers how to build AI-powered applications using the LangChain4j library and Azure OpenAI. It is aimed at Java developers who are new to LLM-based application development and want a guided path from a simple chat interface through prompt engineering, retrieval-augmented generation, tool calling, and the Model Context Protocol.

The course is not a reference implementation or a production framework. It is a teaching repository with working code examples, companion video sessions for each module, and embedded Copilot prompts that encourage learners to explore and extend the examples as they work through the material.

LangChain4j is a Java and Kotlin library for building LLM-powered applications. It differs from the original Python LangChain project by targeting JVM-based ecosystems; the README describes LangChain4j as separate from LangChain, not a port of it.

The Five Modules and What Each Covers

The course is organised into five modules, each in a numbered top-level directory:

01-introduction covers LangChain4j fundamentals: how the library works, how to connect to an Azure OpenAI deployment, and how to send a basic chat message.

02-prompt-engineering covers effective prompt design. The accompanying video session is titled Prompt Engineering with LangChain4j.

03-rag covers retrieval-augmented generation: building knowledge-based systems that supplement a model's answers with retrieved documents.

04-tools covers integrating external tools and building simple assistants that can call those tools.

05-mcp covers the Model Context Protocol, LangChain4j's MCP integration, and agentic patterns.

Each module has a corresponding companion live session video linked from the README. The introduction, prompt engineering, RAG, tools, and MCP modules each have a YouTube live session URL. The module for RAG has its own separate video; tools and MCP share a session titled AI Agents with Tools and MCP.

A Glossary in docs/GLOSSARY.md defines key terms for readers new to LLM concepts.

Getting Started With Codespaces

The recommended setup path uses GitHub Codespaces with a pre-configured devcontainer. The process:

1. Fork the repository to your GitHub account. 2. Click Code, then the Codespaces tab, then the three-dot menu, and select New with options. 3. Use the defaults to select the development container created for this course. 4. Click Create codespace. 5. Wait 5 to 10 minutes for the environment to be ready. 6. Open 01-introduction/README.md to begin.

The devcontainer comes pre-configured with GitHub Copilot for AI-paired programming. Learners can ask Copilot questions suggested in each Java file's header comments and in each module's README exploration prompts throughout the course.

For learners who prefer a local environment, the .env.example file in the repository root shows the required configuration:

AZURE_OPENAI_ENDPOINT="https://<your-resource>.openai.azure.com/"
AZURE_OPENAI_API_KEY="<paste-key-here>"
AZURE_OPENAI_DEPLOYMENT="gpt-5"
AZURE_OPENAI_EMBEDDING_DEPLOYMENT="text-embedding-3-small"

The project includes start-all.sh and start-all.ps1 scripts for starting all services, with stop equivalents, which suggests the exercises involve multiple running services.

Handling the Repository's 50-Plus Translations

The repository includes translations of the README into over 50 languages, maintained by the Co-op Translator tool. This makes a full git clone significantly larger than the course content alone. The README provides a sparse checkout command for learners who want only the course materials:

git clone --filter=blob:none --sparse https://github.com/microsoft/LangChain4j-for-Beginners.git
cd LangChain4j-for-Beginners
git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'

On Windows CMD:

git clone --filter=blob:none --sparse https://github.com/microsoft/LangChain4j-for-Beginners.git
cd LangChain4j-for-Beginners
git sparse-checkout set --no-cone "/*" "!translations" "!translated_images"

This checks out all files in the root and numbered module directories while excluding the translations/ and translated_images/ folders. The sparse checkout gives access to everything needed to complete the course.

LangChain4j vs LangChain and vs Spring AI

LangChain4j occupies a position in the Java ecosystem that LangChain occupies in the Python ecosystem: a library for composing LLM calls, retrieval pipelines, tool calling, and agent patterns into applications. The key difference is that LangChain4j targets Java and Kotlin natively and has its own API design rather than being a translation of the Python library.

Spring AI is the other major Java library for LLM integration, published by VMware and designed to fit into the Spring framework ecosystem. The difference in approach is that Spring AI integrates tightly with Spring Boot's dependency injection, auto-configuration, and testing patterns, while LangChain4j is framework-agnostic. Teams already running Spring Boot applications will find Spring AI's conventions familiar; teams outside the Spring ecosystem or those who prefer explicit configuration may find LangChain4j more straightforward.

This course focuses entirely on LangChain4j with Azure OpenAI. It does not cover Spring AI and does not discuss Ollama, other cloud providers, or self-hosted models. The .env.example requires Azure endpoints and an Azure API key.

Testing, Copilot Integration, and Limitations

After completing the five modules, the README points to a Testing Guide in docs/TESTING.md that covers LangChain4j testing concepts in action. The course includes a pom.xml at the root, suggesting a Maven build structure for the Java code examples.

Each code file in the course includes header comments with suggested Copilot questions. The README describes these as prompts the learner can ask Copilot while the file is open; Copilot has full context of the codebase and can explain, extend, and suggest alternatives based on those prompts. This is a deliberate part of the learning model, not optional enhancement.

The main limitation is the Azure OpenAI dependency. Every exercise in the course assumes a working Azure OpenAI endpoint. Learners who do not have an Azure subscription must create one; the README links to a free Azure account page. There is no documented path for using the course with a non-Azure OpenAI provider or a local model.

The repository has no GitHub releases. The last push was on 2026-09-23. It is MIT-licensed.

Editorial conclusion

This course is the right starting point for Java developers who want to build AI applications with LangChain4j and Azure OpenAI and prefer a structured module sequence over reading library documentation. The Codespaces devcontainer removes local setup friction entirely. Teams that need a fully local or self-hosted LLM backend will find the course assumes Azure OpenAI throughout. Check that your Azure subscription can create OpenAI deployments before starting, and use the sparse checkout command from the README to avoid cloning 50-plus language translation directories.

Frequently asked questions

Official sources

1. Issues
2. License: MIT
3. microsoft/LangChain4j-for-Beginners on GitHub
4. README