10 Java-based tools and frameworks for generative AI

Java is not the first language most programmers think of when they start projects involving artificial intelligence (AI) and machine learning (ML). Many turn first to Python because of the large number of Python-based frameworks and tools for AI, ML, and data science.

But Java also has a place in AI, machine learning, and the generative AI revolution. It’s still a first-choice language for many developers who appreciate its technical advantages and large ecosystem. Those with a belts-and-suspenders instinct like knowing every data element has a well-defined type. The virtual machine is so fast and well-engineered that other languages borrow it. Even some Python lovers run their code with Jython to enjoy the speed of the JVM.

Between them, Sun and Oracle have delivered a 30-year stream of innovation, and Oracle and the OpenJDK project continue to add new features, mostly without breaking the old code. Java’s traditional, slow-but-steady focus on stability and performance means there are many good options for approaching AI and machine learning without leaving the safety of the well-typed Java cocoon.

As this list shows, there are many good options for Java-based teams that need to integrate AI into their applications and workflow. There’s no reason Python coders should have all the fun.

Spring AI

Over the years, Spring has offered a respected foundation for creating anything from web applications to microservices. Now Spring AI aims to make it simpler to bring any kind of AI into this environment by offering a set of abstractions that organize and define the process.

Developers who want to interact with major providers like Anthropic and OpenAI can use Spring AI abstractions to quickly integrate models to handle tasks like chat completion or moderation. All the major model providers, both commercial and open source, are supported.

Developers who want to store data locally in a vector database can plug in directly to any of the dozen or so options like Milvus or Pinecone. Spring AI marshals the data into and out of the embeddings so that developers can work with Java objects while the database stores pure vectors.

Spring AI also has features for various tasks that are rapidly becoming standard in application development. Chat conversations can be automatically stored for later recovery. An AI model evaluation feature supports meta-evaluating models to reduce or at least flag hallucinations.

LangChain4j

Many applications want to integrate vector databases and LLMs into one portal. Oftentimes, one LLM is not enough. Say a generative AI model produces some text and then an image generation LLM illustrates it. At the beginning and end of the pipelines, a moderation AI watches to ensure no one is offended.

LangChain4j is a Java-first version of LangChain, a popular framework in the JavaScript and Python communities. The code acts as a nexus for unifying all the different parts that developers need to integrate. Dozens of different models and datastores are bundled together with a few standard, powerful abstractions.

Deeplearning4J

Java developers who are tackling an AI classification project can turn to the Eclipse Deeplearning4J (DL4J) ecosystem, which supports a wide range of machine learning algorithms. In goes raw data and out comes a fully tuned model ready to make decisions.

The core of the system is libnd4j, a C++ library that ensures fast execution of the core machine learning primitives. It’s driven by nd4j and Samediff, two bundles of graphing, NumPy, and TensorFlow/PyTorch operations that can be linked together to implement the machine learning algorithm. The dataflows are defined by Apache Spark.

While the overall framework is unified by Java, many of Deeplearning4J’s moving parts may be written in a different language. The pipeline is designed to be open to experimentation in many languages that can work directly with the JVM like Kotlin or Scala. Python algorithms can be run directly in Python4j.

The project is open source and its documentation is filled with many good examples that unlock the power of its components.

Apache Spark MLib

Data scientists working through large problem sets have long turned to Spark, an Apache project that’s designed to support large-scale data analysis. MLib is an extra layer that’s optimized for machine learning algorithms.

The data can be stored in any Hadoop-style storage location. The algorithms can be coded in any of the major languages. Java, Scala, or any of the JVM-focused languages are a natural fit. But Spark users have also added the glue code to use Python or R because they’re so popular for data analysis.

An important aspect of what makes MLib attractive is its prebuilt routines for many classic machine learning and data analysis algorithms—decision trees, clustering, alternating least squares, and dozens of other classics. Big computations like singular value decompositions of massive matrices can be spread across multiple machines to speed up everything. Many developers don’t need to implement much code at all.

Spark handles the rest with a pipeline that’s engineered for iterative processes. MLib’s developers have focused on speed enough to brag that it’s often 100 times faster than MapReduce. That’s the real attraction.

Testcontainers

Much of the LLM ecosystem runs inside Docker containers, so a tool that helps juggle them all is useful. Testcontainers is an open source library that starts up, shuts down, and manages the IO and other channels for your containers. It’s one of the easiest ways to integrate LLMs with your stack. And if you need a database, service bus, message broker, or any other common component, Testcontainers has many predefined modules ready to fire them up.

GraalPy

Yes, it looks like something for Python code, and it is. GraalPy is an embeddable version of Python3 that’s optimized to make it easier to run Python code inside the JVM. Java programmers can leverage all the various Python libraries and tools. GraalPy, which claims the fastest execution speed for Python inside a JVM, is part of the larger GrallVM collection of projects designed to deploy and maintain stacks in virtual machines.

Apache OpenNLP

Learning from text requires plenty of preprocessing. Such text needs to be cleaned of extraneous typesetting commands, organized into sections, and separated into small chunks so the algorithms can begin to extract meaning from patterns. This is where Apache OpenNLP steps in, with many basic algorithms for building a solid foundation for machine learning.

The tools run the gamut from low-level segmentation and tokenization to higher-level parsing. Extras like language detection or named-entity extraction are ready to be deployed as needed. Models for more than 32 languages are included in OpenNLP’s JAR files, or you can start training your own.

The tool is well-integrated with the Java ecosystem. Projects like UIMA or Solr are already leveraging OpenNLP to unlock the patterns in natural language text. Integration with Maven and Gradle makes starting up simple.

Neo4j

When the application calls for a RAG datastore, Neo4j is a graph database that can handle the workload. Neo4j already supports various graph applications like fraud detection or social network management. Its solid Java foundation makes it easy to integrate both RAG applications and graph databases with Java stacks in a single, unified datastore that it calls GraphRAG.

Stanford CoreNLP

Stanford CoreNLP, from the Stanford NLP Group, is another collection of natural language routines that handle most of the chores of taking apart big blocks of text so it can be fed to a machine learning algorithm. It does anything from segmentation to normalizing standard parts of speech like numbers or dates.

Developers look to CoreNLP for its higher accuracy and pre-built options. Models for sentiment analysis or coreference detection, for example, are ready to go. More complicated parsing algorithms and strategies are easier to implement with the advanced features of the library.

The package is easy to include with Gradle or Maven. Models for nine major languages are ready to go.

Jllama

Sometimes it makes sense to run your model in a JVM that you control and supervise. Perhaps the hardware is cheaper. Perhaps the privacy and the security are simpler. Firing up the LLM locally can have many advantages over trusting some distant API in a faraway corner of the cloud.

Jllama will load up many of the most popular open source models and run inference with them. If your application needs chatting, prompt completion, or an OpenAI-compatible API, Jllama will deliver a response. The code will also download and start up any number of prequantized models like Gemma, Llama, Qwen, or Granite.

The code leverages some of the newest Java features like the Vector API and the SIMD-aware extensions that can speed up the parallel executions for LLM inference. The code will also divide the workload into parts that can be distributed around the available computational resources in a cluster.