langchain4j

Sun, 08 Jun 2025 15:28:32 +0800

langchain4j/langchain4j

LangChain for Java: Supercharge your Java application with the power of LLMs

Introduction

Welcome!

The goal of LangChain4j is to simplify integrating LLMs into Java applications.

Here’s how:

Unified APIs: LLM providers (like OpenAI or Google Vertex AI) and embedding (vector) stores (such as Pinecone or Milvus) use proprietary APIs. LangChain4j offers a unified API to avoid the need for learning and implementing specific APIs for each of them. To experiment with different LLMs or embedding stores, you can easily switch between them without the need to rewrite your code. LangChain4j currently supports 15+ popular LLM providers and 15+ embedding stores.
Comprehensive Toolbox: Since early 2023, the community has been building numerous LLM-powered applications, identifying common abstractions, patterns, and techniques. LangChain4j has refined these into practical code. Our toolbox includes tools ranging from low-level prompt templating, chat memory management, and function calling to high-level patterns like Agents and RAG. For each abstraction, we provide an interface along with multiple ready-to-use implementations based on common techniques. Whether you’re building a chatbot or developing a RAG with a complete pipeline from data ingestion to retrieval, LangChain4j offers a wide variety of options.
Numerous Examples: These examples showcase how to begin creating various LLM-powered applications, providing inspiration and enabling you to start building quickly.

LangChain4j began development in early 2023 amid the ChatGPT hype. We noticed a lack of Java counterparts to the numerous Python and JavaScript LLM libraries and frameworks, and we had to fix that! Although “LangChain” is in our name, the project is a fusion of ideas and concepts from LangChain, Haystack, LlamaIndex, and the broader community, spiced up with a touch of our own innovation.

We actively monitor community developments, aiming to quickly incorporate new techniques and integrations, ensuring you stay up-to-date. The library is under active development. While some features are still being worked on, the core functionality is in place, allowing you to start building LLM-powered apps now!

Documentation

Documentation can be found here.

The documentation chatbot (experimental) can be found here.

Getting Started

Getting started guide can be found here.

Code Examples

Please see examples of how LangChain4j can be used in langchain4j-examples repo:

Examples in plain Java
Examples with Quarkus (uses quarkus-langchain4j dependency)
Example with Spring Boot
Examples with Helidon (uses io.helidon.integrations.langchain4j dependency)
Examples with Micronaut (uses micronaut-langchain4j dependency)

Useful Materials

Useful materials can be found here.

Get Help

Please use Discord or GitHub discussions to get help.

Request Features

Please let us know what features you need by opening an issue.

Contribute

Contribution guidelines can be found here.

agents-course

Thu, 08 May 2025 15:30:23 +0800

huggingface/agents-course

The Hugging Face Agents Course

If you like the course, don’t hesitate to ⭐ star this repository. This helps us to make the course more visible 🤗.

Content

The course is divided into 4 units. These will take you from the basics of agents to a final assignment with a benchmark.

You can access the course here 👉 https://hf.co/learn/agents-course

Unit	Topic	Description
0	Welcome to the Course	Welcome, guidelines, necessary tools, and course overview.
1	Introduction to Agents	Definition of agents, LLMs, model family tree, and special tokens.
1 Bonus	Fine-tuning an LLM for Function-calling	Learn how to fine-tune an LLM for Function-Calling
2	Frameworks for AI Agents	Overview of `smolagents`, `LangGraph` and `LlamaIndex`.
2.1	The Smolagents Framework	Learn how to build effective agents using the `smolagents` library, a lightweight framework for creating capable AI agents.
2.2	The LlamaIndex Framework	Learn how to build LLM-powered agents over your data using indexes and workflows using the `LlamaIndex` toolkit.
2.3	The LangGraph Framework	Learn how to build production-ready applications using the `LangGraph` framework giving you control tools over the flow of your agent.
2 Bonus	Observability and Evaluation	Learn how to trace and evaluate your agents.
3	Use Case for Agentic RAG	Learn how to use Agentic RAG to help agents respond to different use cases using various frameworks.
4	Final Project - Create, Test and Certify Your Agent	Automated evaluation of agents and leaderboard with student results.

Prerequisites

Basic knowledge of Python
Basic knowledge of LLMs

Contribution Guidelines

If you want to contribute to this course, you’re welcome to do so. Feel free to open an issue or join the discussion in the Discord. For specific contributions, here are some guidelines:

Small typo and grammar fixes

If you find a small typo or grammar mistake, please fix it yourself and submit a pull request. This is very helpful for students.

New unit

If you want to add a new unit, please create an issue in the repository, describe the unit, and why it should be added. We will discuss it and if it’s a good addition, we can collaborate on it.

Citing the project

To cite this repository in publications:

@misc{agents-course,
  author = {Burtenshaw, Ben and Thomas, Joffrey and Simonini, Thomas and Paniego, Sergio},
  title = {The Hugging Face Agents Course},
  year = {2025},
  howpublished = {\url{https://github.com/huggingface/agents-course}},
  note = {GitHub repository},
}

Function Calling on Producthunt daily