Spring AI has emerged as a powerful framework for integrating artificial intelligence into Java applications built on the Spring ecosystem. By providing abstractions over various language model providers, it allows developers to leverage conversational AI, retrieval-augmented generation (RAG), and agentic workflows using familiar Spring patterns. This guide explores the key components and capabilities of Spring AI, drawing from a curated series of tutorials that cover everything from core chat functionality to advanced integrations like the Model Context Protocol (MCP).
Getting Started with Spring AI
The foundation of Spring AI lies in its intuitive abstractions for interacting with large language models (LLMs). The ChatClient Fluent API provides a streamlined way to send prompts and receive responses, while Chat Memory enables context retention across conversations. Developers can also implement streaming responses for real-time output, configure multiple LLMs for different use cases, and extract structured output (e.g., JSON) from model responses. These core features make Spring AI accessible for beginners while remaining flexible for advanced scenarios.
AI Models and Providers
Spring AI supports a wide range of LLM providers, allowing developers to choose the best model for their application. Integration guides cover:
- Anthropic’s Claude models for safety-focused conversational AI
- Google Cloud AI services for enterprise-grade scalability
- DeepSeek models for cost-effective inference
- Hugging Face models via Ollama for local deployment
- Ollama itself for running models like Llama 2 or Mistral locally
This provider-agnostic approach ensures that applications remain portable and can switch between models without significant code changes.
RAG and Vector Stores
Retrieval-Augmented Generation (RAG) enhances LLM responses by incorporating external knowledge. Spring AI provides a robust Embeddings Model API for converting text into vector representations, which are then stored in vector databases. Supported vector stores include Redis, PGVector (PostgreSQL), ChromaDB, and MongoDB. Tutorials demonstrate how to build a complete RAG application using these stores, enabling semantic search and context-aware answers without fine-tuning.
Advisors and AI Agents
Spring AI introduces Advisors as a pattern for injecting cross-cutting concerns into AI workflows. Recursive Advisors allow chaining multiple advisors for complex logic. For building autonomous agents, Spring AI provides tools to create effective agents with tool-calling capabilities, including explainable AI agents that capture LLM tool call reasoning. These patterns enable agentic workflows that can plan, execute actions, and adapt based on feedback.
Model Context Protocol (MCP)
The Model Context Protocol is a standardized way for AI applications to interact with external tools and data sources. Spring AI’s MCP support includes annotations for defining tools, managing elicitation (prompt construction), and implementing authorization via OAuth2. Security is a key focus, with guides on securing MCP servers using OAuth2 to protect sensitive operations.
Multimodal and Advanced Capabilities
Beyond text, Spring AI extends to multimodal inputs. Developers can extract structured data from images using vision models, transcribe audio files with OpenAI’s Whisper, and implement Text-to-SQL for natural language database queries. The function calling feature allows LLMs to invoke Java methods, demonstrated with the Mistral AI API. Additionally, evaluators help test and validate LLM responses for reliability.
Series Overview
This guide is based on an extensive series of tutorials published on Baeldung, covering each of the topics above in depth. The series starts with foundational concepts and progresses to advanced integrations, providing practical code examples and best practices. Whether you are new to Spring AI or looking to expand your knowledge, these resources offer a structured path from basic chat interactions to sophisticated AI agents and multimodal applications.
By leveraging Spring AI, Java developers can build production-ready AI solutions that are maintainable, scalable, and aligned with enterprise standards. The framework’s design emphasizes modularity and testability, making it an ideal choice for integrating AI into existing Spring Boot applications.