Top AI Tools and Libraries for Java Developers in 2025

AI is becoming a crucial part of how we build software. From recommending products to helping businesses predict what their customers might need, AI is changing how we think about building and using apps and software’s.

The wide variety of libraries and frameworks available in the Java ecosystem will bring developers powerful, specific tools for creating AI applications that enable a variety of subjects, including machine learning and natural language processing. Whether you’re just a beginner with machine learning or looking to level up your current projects, this blog will help you to go through some of the best AI tools and libraries available to Java developers today.

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AI for Java: key benefits and strength

Here are the key benefits of using AI for Java Development, it includes:

• Code Completion: Ai tools can understand what you are writing and it provides suggestions as you type. You will spent less time in debugging, fewer typos, and more focusing on actual problem you are solving .

• Automated Testing: Ai can generate test cases for your java code automatically which means more reliable apps and less time spent writing repetitive test codes.

• Good for Learning Purpose: Ai helps junior developer or someone new to java by giving documentation and explanation. Instead of searching on Stack Overflow, learners can just ask to Ai assistant and you will be provided with relevant answers right when you need them.

There are many libraries that enable AI process that belongs to rich ecosystem of Java. Here are a few of the most notable libraries:

Discover how AI is transforming the way developers code. Explore the top AI coding tools.

1. Deeplearning4j

   

   Deeplearning4j is an open source, powerful, deep learning library on JVM. It supports all types of neural network architectures and is designed for both research and production deployment.

   Deeplearning4j provides java developers full access to deep learning tools without needing to switch languages. If you are working on backend system or handling big data in Java, DL4J lets you add AI without leaving the comfort of the JVM.

Key Features:

• Scalability: It can be scaled easily from a single node up to a large-scale distributed system, which means you can use it to solve complex deep learning problems with ease.
• Integration: It is integrated with popular big data frameworks such as Hadoop and Apache Spark for efficient data processing.
• Flexibility: It supports many types of neural network, including feedforward neural networks and complex convolutional and recurrent neural networks.

Use Cases:

• Computer Vision: Image recognition, object detection are also done in deeplearning4j.
• Anomaly detection: Discovering unusual behaviour in the data set.
• Recommendation Systems: Recommending products or content to users.

2. Keploy

   

   Keploy is an open-source robust API testing platform designed for creating test cases and data mocks from API calls. It’s particularly useful for testing and mocking in microservice architecture.

   It helps java developers working with AI to build more reliable, testable, and maintainable applications by automating the testing process and providing realistic data of the test.

   Key Features :

   • Automatic test generation: It Generate text based on existing code and usage patterns.

   • Data Mocks: It creates data mocks based on actual Api responses and also helps in creating various AI prediction scenarios.

   • Supports multiple frameworks: It can work with popular frameworks like Java, Node.js, Python, Golang, and many more.

   Use Cases:

   • Ai model testing: It Test Java Api’s that serve AI models and validate request/response patterns, and also it ensures consistent model serving behavior.

   • Regression testing: It detects Api changes and also ensures that updates don’t break existing functionality.
   • Automated test creation: It is fully automated and prompt engineered.

3. Apache OpenNLP:

   

   Apache OpenNLP is a powerful, open-source Java library designed for natural language processing (NLP),a specialized field of artificial intelligence (AI). It uses machine learning to process and analyze human language, making it an excellent tool for building AI applications that understand and generate text.

   It’s written in java, so it integrates seamlessly into java-based projects, offering developers a robust toolkit to handle complex language tasks.

Key Features:

• Tokenization: It breaks down text into single words, punctuations, or other units.
• Detection of sentences: Detection of the start and ending position of each sentence within some block of text.
• Parsing: Analyzes the grammatical structure of sentences.

Use Cases:

• Text classification: Categorizing documents, sentiment analysis, or spam detection.
• Tokenization: Breaking text into words, sentences or paragraphs.
• Language Detection: Identifying the language of a text.

4. Weka

   

Weka (Waikato Environment for Knowledge Analysis) is a free, open-source machine learning and data mining software written in Java. It’s widely used for data preprocessing, classification, regression, clustering, association rules, and visualization.
Weka can be use through its GUI, As a Java API or even from the command line interface.

Key Features:

• Data Preprocessing: Its includes tools for data cleaning, normalization, attribute selection, discretization and handling missing values, preparing dataset for analysis.
• Visualization Tools: Features visualization like scatter plots, histograms and decision tree graphs to help users explore and interpret data.
• API For Java Integration: It can be embedded in Java Application.

Use Cases:

• Machine Learning Research: Testing and comparing algorithm performance.
• Drug Discover: Analysing molecular data to identify potential drug candidates.
• Sentiment Analysis: Classifying customer feedback and reviews.

5. Spring AI

   

Spring AI is an application framework developed by the Spring team to simplify the creation of AI- powered applications in Java easier and more efficiently. It provides tools to integrate AI functionalities- such as chat models, text-to-image generation, chat completion, audio transcription and embeddings into your projects without unnecessary complexity.

Spring AI brings these concepts for the java ecosystem, focusing on portability, modularity, and ease of use with spring boot.

Key features:

• Portable APIs: Spring AI supports multiple AI providers including OpenAI, Azure OpenAi
for chat, image generation, and embedding models.
• Abstraction: It offers an interface like ChatClient to interact with Ai models.
• Spring Boot Integration: Spring AI comes with auto-configuration and starters allowing you
to quickly set up AI features in Spring Boot applications with less setup.

Use Cases:

• LLM integration: It Easily connects your applications to large language models like
OpenAI’s GPT models.
• Prompt Engineering: Managing and optimizing prompt to enhance the performance of your
AI models.
• AI powered applications: Build smarter applications with AI features, such as automated customer support, content generation, Personalized recommendations and more.

6. GitHub Copilot

   

GitHub Copilot is an AI pair programmer or a coding assistant that integrates with IDEs to help Java Developers write code faster and accurately. It offers intelligent code suggestions, writes code for you, and generates test cases which makes development more efficient.
It works inside your code editor like VS Code or Jetbrains IDEs and supports many programming languages as well.

Key Features:

• Test generation: Copilot can create an entire Junit test case for your code based on the implementation and it helps in achieving better test coverage with less effort.
• Framework Assistance: It offers suggestions for popular Java frameworks like Spring, Hibernate, and Jakarta EE.
• Code Completion: Copilot provides context-aware suggestions for methods, classes, and code implementations as you type.
It also understands Java syntax, conventions, and common patterns.

Use Cases:

• Code completion: It provides real-time suggestions as you code.
• Learning from suggestions: GitHub Copilot can be used as a learning tool to discover
new patterns, techniques and different approaches in java.
• Beneficial for Developers: It can be used for generating the code while building
AI applications.

7. Tabnine

   

Tabnine is an AI-powered code completion tool that uses deep learning algorithms to predict and suggest code snippets as you type. By analyzing vast amounts of code data, tabnine streamlines the coding process, allowing developers to focus on creativity and problem-solving rather than the minor syntax.

It works with multiple programming languages, including Java, and it works smoothly with popular IDEs like IntelliJ IDEA, VS Code, and Eclipse.

Key Features:

• Intelligent Code Completions: Tabnine offers context-aware code suggestions, helping you write code faster by predicting the next snippet based on your current context.
• Framework-Specific Features: Tabnine integrates with a wide range of libraries and tools. It ensures that developers can leverage AI without altering in their existing workflow.
• Privacy and Security: It offers code security for local AI models that run directly on your machine to ensure code stays private and secure.

Use Cases:

• Prevention of error: It helps to correct syntax and logic errors early.
• High speed: The suggestions appear in milliseconds, that makes our work faster and smoother.

Conclusion:

The tools and libraries that I wrote in this blog are useful for a wide range of tasks in AI and machine learning, including deep learning and neural networks, natural language processing, etc. The choice of tools or library will depend on the specific needs of your project and the type of AI project you are working on.

These libraries are updated regularly as AI continues to evolve to include the latest research and techniques. These tools are useful for developers to build useful applications that use artificial intelligence to automate tasks and enhance productivity.

FAQs:

What makes Java a good choice for AI development?

Java is a good choice for AI development because it is robust, platform-independent, has a strong community, and offers many libraries and frameworks for AI projects.

Can AI tools for coding be used by beginners?

Yes, AI tools for coding are suitable for beginners as they help correct errors and speed up the coding process by giving suggestions to users.

What are the drawbacks of using Java for AI development?

Java isn’t as popular as Python for AI because it doesn’t have as many libraries or a large support community. Plus, its longer and more complex code can make it harder to test faster and make changes, which is a big part of working in AI.

What are the future of AI in Java ?

Java is growing faster in the AI, with new libraries emerging and smoother integration with popular machine learning frameworks. By 2026, you can expect java to work even more effortlessly with tools like Deeplearning4j and have smarter, AI-integrated features build into the IDEs.