A comprehensive framework for building LLM-powered applications in Java, with excellent support for Ollama as a model provider.
Never create a new HTTP client per request. Reuse a single instance with a large connection pool. The difference can be a 3.2× increase in QPS.
Here is a guide on how to get Ollama working with Java.
: A Java version of the LangChain framework that includes a Quarkus-integrated Ollama chat model for building complex AI agents. Workflow Overview Ollama - GitHub ollamac java work
A popular Java wrapper for the Ollama API, allowing you to easily list models, chat, generate embeddings, and pull new models from the library.
public Flux<String> chat(String sessionId, String userMessage) List<ChatMessage> history = sessions.computeIfAbsent(sessionId, id -> new ArrayList<>()); history.add(new ChatMessage(ChatRole.USER, userMessage));
Java's strengths—platform independence, concurrency, massive open-source library ecosystem, and a strong emphasis on enterprise-grade stability—make it an ideal language for building upon local LLMs. Developers can embed AI into existing Spring Boot microservices, build secure desktop applications with JavaFX, or use powerful data-processing frameworks like Apache Flink in conjunction with local models. The Java ecosystem has responded with several dedicated libraries and frameworks for this very purpose. The difference can be a 3
Practical example: A Spring Boot backend can send prompts to an Ollama instance via HttpClient, process streamed tokens asynchronously, and push results to clients over SSE or WebSocket.
While the term "" specifically refers to a native macOS desktop client for Ollama , Java developers primarily interact with Ollama through dedicated libraries and frameworks. Key Java Libraries for Ollama
String jsonString = JSON.toJSONString(chatRequest); MediaType jsonType = MediaType.get("application/json; charset=utf-8"); RequestBody body = RequestBody.create(jsonString, jsonType); Workflow Overview Ollama - GitHub A popular Java
Modern LLMs support powerful advanced features. Two of the most impactful are (or Function Calling) and JSON Mode .
This article explores how together, providing a complete guide to setting up, integrating, and deploying local LLMs in a Java environment. What is Ollama?
This article explains how Ollama functions alongside Java, the architecture that powers this connection, and the exact steps needed to build local AI applications. How the Ollama-Java Ecosystem Works