Spring AI is a comprehensive framework designed to simplify the integration of artificial intelligence capabilities into Java applications built on the Spring platform. It provides a consistent abstraction layer over various language model providers, enabling developers to add conversational interfaces, retrieval-augmented generation (RAG), and agentic workflows without leaving the familiar Spring ecosystem. This guide offers an overview of Spring AI's core modules and advanced patterns, drawing from a curated series of tutorials that cover everything from basic chat memory to the Model Context Protocol.
Getting Started with Spring AI
The journey begins with understanding the foundational components. Spring AI's ChatClient Fluent API offers a type-safe, chainable interface for crafting conversations. You'll learn how to manage conversation memory to maintain context across turns, handle streaming responses for real-time updates, and configure multiple LLMs to suit different tasks. A key feature is structured output, which lets you extract data from model responses in a predefined format—ideal for automation. For a hands-on introduction, refer to the Getting Started section series.
AI Models and Providers
Spring AI supports a growing list of model providers, each with its own strengths. The framework abstracts provider-specific APIs, so you can switch between them with minimal code changes. Tutorials cover using Anthropic's Claude for safe and capable conversations, Google Cloud AI for enterprise-grade deployments, and DeepSeek for cost-effective performance. Locally, you can run models via Ollama to create a ChatGPT-like chatbot, or tap into the vast library of Hugging Face models. These guides demonstrate how to configure API keys, set up clients, and build interactive bots.
RAG and Vector Stores
Retrieval Augmented Generation (RAG) enhances AI responses by fetching relevant external information before generating an answer. Spring AI provides an Embeddings Model API to convert text into vector representations, which are stored in vector databases. You can build a RAG application using Redis for in-memory speed, PGVector for PostgreSQL integration, ChromaDB for simplicity, or MongoDB for a document-oriented approach. Each vector store has a dedicated guide that walks through setup, embedding generation, and semantic search implementation.
Advisors and AI Agents
As applications grow, you need components that orchestrate complex interactions. Spring AI's Advisors are modular building blocks that wrap around chat clients to add cross-cutting concerns like logging, safety checks, and memory management. Recursive Advisors allow you to chain multiple advisors for sophisticated behavior. Beyond that, you can create AI agents that autonomously decide when to call tools or ask for clarification. The series includes guides on building effective agents, capturing LLM tool call reasoning for explainability, and implementing a full AI assistant that follows instructions and uses external APIs.
Model Context Protocol (MCP)
The Model Context Protocol (MCP) is a standard for managing context across different stages of an AI workflow. Spring AI simplifies MCP integration with dedicated annotations and authorization support. You'll learn how to explore MCP concepts, use MCP annotations to define context sources, and handle elicitations (prompts that trigger context updates). Security is also covered: the frameworks supports OAuth2 for both MCP authorization and server protection, ensuring that sensitive contextual data remains secure.
Multimodal and Advanced Capabilities
Modern AI applications often need to work with more than just text. Spring AI enables structured data extraction from images using vision models, making it possible to parse receipts or forms automatically. You can also test LLM responses with evaluators to validate quality and consistency. For database queries, the Text-to-SQL feature lets you ask natural language questions and get SQL commands. Audio processing is supported via transcription with OpenAI's Whisper model. Finally, function calling is demonstrated using Mistral AI's API, allowing your application to invoke external services in a structured manner.
Spring AI empowers Java developers to build production-ready AI features with confidence. By leveraging the Spring platform's proven patterns—dependency injection, modularity, and testability—you can create applications that are both powerful and maintainable. Each of the topics mentioned above is explored in depth in the corresponding tutorials, providing step-by-step instructions and code examples.