For complex AI workflows, is a rising star. It brings the popular LangChain concept to the Java world, offering high-level abstractions for "AI-powered" tasks. It provides components for chains, RAG (Retrieval-Augmented Generation), agents, and tools (function calling), making it easier to build sophisticated applications like document Q&A systems or AI agents that can take actions.
LangChain4j is currently the most popular, production-ready framework for building LLM applications in the Java ecosystem. Modeled loosely after Python's LangChain but rewritten from scratch for Java, it provides an elegant, structured approach to working with Ollama. It supports chat memory, streaming responses, tool calling, and structured outputs out of the box. 2. Spring AI ollamac java work
spring: ai: ollama: base-url: http://localhost:11434 chat: options: model: llama3 temperature: 0.8 For complex AI workflows, is a rising star
With this, you can create an OllamaChatModel and use it with LangChain4j's AiServices to create powerful agents that can interact with your business logic. This is the go-to choice for teams looking to build the next generation of "AI-native" applications. LangChain4j is currently the most popular