Survey of Open-Source Java Frameworks for Deep Learning, Machine Learning, and LLMs

Introduction

This report surveys open-source Java-based frameworks for Deep Learning (DL), Machine Learning (ML), and Large Language Models (LLMs). While Java is less dominant than Python in ML/DL, its strengths in enterprise-grade systems, cross-platform compatibility, and security have produced several strong open-source frameworks. These typically support the JVM ecosystem and suit enterprise applications, embedded systems, and integration with existing Java infrastructure. The survey covers only open-source projects, based on GitHub repositories, official documentation, and community discussions as of October 25, 2025.

Machine Learning Frameworks

Weka

• The classic Java ML library, developed at the University of Waikato since 1993; latest releases 3.8.x (2025).
• GUI (Explorer) for visual data mining, plus CLI and API. Covers preprocessing, classification, regression, clustering, association rules, and ARFF format.
• Hundreds of algorithms: J48 decision trees, SVM, random forests, k-means, Apriori, etc.
• Strengths: user-friendly GUI, comprehensive algorithms (~1.5k GitHub stars). Weaknesses: limited scalability on very large data.

Apache Mahout

• Distributed ML framework under the Apache Foundation, started 2008; now focused on Spark and a Scala DSL. Version 0.4 released April 2025.
• Distributed linear algebra with modular solvers and CPU/GPU acceleration; clustering, classification, recommender systems, matrix factorization.
• Strengths: scalability for big data. Weaknesses: steep learning curve.

Tribuo

• ML library from Oracle Labs emphasizing production readiness; open-sourced 2019, version 4.3 (2025).
• Type-safe unified API, provenance tracking, ONNX support; integrates XGBoost, LibLinear, TensorFlow; imports scikit-learn/PyTorch models.
• Strengths: reproducibility, Python-Java interoperability. Weaknesses: smaller community (~1k stars).

Smile

• High-performance ML engine (~6k stars) covering classification, regression, clustering, NLP (Word2Vec, POS tagging, sentiment), genetic algorithms, and even Llama 3 on the JVM.
• Supports Java/Scala/Clojure/Groovy. Reported to be several times faster than R/Python equivalents. No GUI; technical documentation.

Java-ML

• Simple collection of ML algorithms with a unified interface (~500 stars); classification, clustering, feature selection; optional Weka/LibSVM integration. GPL-licensed; no release packages.

MOA (Massive Online Analysis)

• Data stream mining from the University of Waikato (MIT Press book). Supports classification, regression, clustering, outlier detection, concept drift, and recommendations; integrates with CapyMOA, River, DeepRiver, streamDM, and ADAMS.

ELKI

• Clustering and outlier detection toolkit (AGPLv3, version 0.8.0 in 2025) with R*-tree indexing, highly parameterized algorithms, and arbitrary data types/distance metrics. Cited by 220+ publications; API unstable until 1.0.

KNIME

• Open-source visual analytics platform (since 2006) with drag-and-drop workflows, 300+ connectors, LLM access, and Python/R integration. Used by enterprises such as SIEMENS.

Deep Learning Frameworks

Deeplearning4j (DL4J)

• Eclipse Foundation project (Apache 2.0, since 2014, ~9k stars). Trains CNNs/RNNs directly in Java; imports PyTorch/TensorFlow/Keras models.
• Ecosystem: SameDiff, ND4J (NumPy-like), LibND4J, Apache Spark, DataVec, Python4j. Suited to JVM microservices, mobile/IoT, and Spark pipelines.

Deep Java Library (DJL)

• AWS open-source project (2019, ~3.5k stars). Engine-agnostic API over TensorFlow, PyTorch, and MXNet, with a model zoo for CV, NLP, and time series. Simplifies engine switching but inherits underlying engine performance.

Encog

• Neural network framework by Jeff Heaton (2008); pure Java/C#, multi-threaded training. Covers NNs, SVM, Bayesian networks, HMMs, genetic programming, NEAT/HyperNEAT. No GPU support.

Neuroph

• Easy-to-use Java neural network library (since 2008) with Neuroph Studio GUI; supports feedforward/feedback networks, Hopfield, and self-organizing maps. Good for education and image recognition.

LLM Frameworks

LangChain4j

• Java implementation of LangChain (open-sourced 2023, ~2k stars). Chains, agents, and RAG support; providers include OpenAI, HuggingFace, and Ollama; integrates with Spring Boot, Quarkus, and vector stores like Pinecone.

Spring AI

• Spring ecosystem AI framework (2023). POJO-based API connecting data/APIs with AI models (OpenAI, Azure); chat and embeddings, vector stores, Spring Boot integration.

OpenNLP

• Apache NLP library (since 2006): tokenization, POS tagging, NER, sentence detection using max-entropy and perceptron models. Lightweight but not focused on modern LLMs.

Framework Comparison

Ratings 1-10, based on 2025 benchmarks:

| Framework | Type | Ease of use | Scalability | Community | Performance | Best for | |---|---|---|---|---|---|---| | Weka | ML | 9 | 6 | 8 | 7 | Education/research | | Mahout | ML | 6 | 9 | 7 | 8 | Big data distributed | | Tribuo | ML | 8 | 8 | 6 | 8 | Production deployment | | Smile | ML/DL | 7 | 8 | 8 | 9 | Comprehensive ML/NLP | | DL4J | DL | 7 | 9 | 8 | 8 | JVM deployment | | DJL | DL | 8 | 8 | 7 | 8 | Engine-agnostic DL | | Encog | DL | 6 | 7 | 6 | 7 | NN research | | LangChain4j | LLM | 8 | 7 | 7 | 7 | LLM app integration | | Spring AI | LLM | 9 | 8 | 9 | 8 | Spring enterprise AI |

Conclusion

Java open-source ML/DL/LLM frameworks are fewer than Python's, but DL4J, DJL, and LangChain4j provide strong support for enterprise scenarios. Recommendations: Weka or Smile for research, Tribuo or DL4J for production, LangChain4j or Spring AI for LLM applications. Future trends point to more LLM integrations and performance optimizations.