Spring AI
An AI application engineering framework officially released by the Spring project, deeply integrated into the Spring ecosystem. It provides Java developers with a unified interface for large model integration, supporting prompt management, model connectivity, RAG, and Agent capabilities through a provider-agnostic unified API design.
Core Facts
Spring officially launched the Spring AI framework for AI integration, seamlessly connecting with the Spring Boot ecosystem.
Spring AI supports multiple model providers including OpenAI, Anthropic, and Ollama, providing a unified API abstraction layer.
Spring AI decouples upper-level business code from specific model implementations by defining unified core abstract interfaces such as ChatModel and EmbeddingModel.
Timeline (last 90 days)
Spring AI supports a multi-level Chat Memory mechanism, and memories can be stored in memory or persisted to various databases.
Spring AI supports configuring log monitoring and evaluation mechanisms for model calls, helping developers track model performance in production environments.
When implementing Tool Calling, Spring AI already adopts encapsulation logic similar to MCP, so when developers migrate from Tool Calling to MCP they only need to adjust the communication layer, and the core business logic hardly needs rewriting.
The combination of DeepSeek + Ollama + Spring AI represents the mainstream path for Java developers to integrate local large models today.
The project's technology stack includes Ollama (local deployment tool), DeepSeek inference model, Spring Boot 3 + Spring AI, and MySQL (for persistent storage of conversational data).
This hands-on course uses DeepSeek paired with Spring AI to develop an offline AI family doctor application.
This knowledge base system adopts a front-end and back-end separated architecture, with the front end using Vue 3 and the back end connecting to large models based on the Spring AI framework
Python remains the absolute mainstream in AI development, and the proportion of companies using Java and Spring AI for AI development is relatively small.
This Spring AI tutorial is divided into three chapters — foundational, advanced, and practical — covering topics such as large model principles, RAG, private deployment, fine-tuning, intelligent Agents, digital humans, enterprise knowledge bases, and medical large models.
Bilibili UP blogger Xu Shu spent nearly two months creating a Spring AI tutorial series.
13 more timeline events
All Facts (20)
Spring officially launched the Spring AI framework for AI integration, seamlessly connecting with the Spring Boot ecosystem.
80%VerifiedSpring AI supports multiple model providers including OpenAI, Anthropic, and Ollama, providing a unified API abstraction layer.
80%VerifiedSpring AI decouples upper-level business code from specific model implementations by defining unified core abstract interfaces such as ChatModel and EmbeddingModel.
80%VerifiedSpring AI Alibaba是阿里巴巴基于Spring AI扩展的框架，支持智能体开发
80%VerifiedAgent development frameworks include LangChain, LangGraph, and DeepAgents within the LangChain ecosystem, as well as Spring AI and Spring AI Alibaba for Java developers.
75%VerifiedSpring AI was launched in 2023 by the Spring team under VMware (now Broadcom).
75%VerifiedThe Spring AI team created a Java version of the MCP SDK and built Spring features on top of it.
70%VerifiedSpring AI provides a standardized FunctionCallback interface for Tool Calling implementation
70%VerifiedSpring AI provides a unified VectorStore interface abstraction, allowing developers to switch between different vector databases without modifying business logic code.
70%VerifiedMCP implements runtime dynamic tool discovery through the tools/list method without requiring hardcoded interfaces, which is a core advantage over traditional REST API integration.
65%VerifiedSpring AI has included built-in MCP client support since version 1.0.
65%VerifiedUsing Spring AI requires Spring Boot 3.x or above, JDK 17 or above, and Maven 3.9 or above.
65%VerifiedUsing Spring AI requires JDK version 17 or above.
65%VerifiedSpring AI's structured output feature can automatically encapsulate large model responses into Java objects, supporting JSON format mapping to POJOs.
65%VerifiedSpring AI's design philosophy directly draws inspiration from LangChain's modular approach.
65%VerifiedAlibaba's Agent Framework is fully compatible with Spring AI and provides Graph-based workflow orchestration capabilities and built-in pre-made agents.
65%UnverifiedSpring AI要求Spring Boot 3.x版本，低于此版本会报错
90%UnverifiedSpring AI内置了对PGVector、Milvus、Chroma等向量存储的支持
85%UnverifiedSpring AI中Tool的注册方式主要有两种：动态绑定（运行时根据请求动态加载工具）和静态绑定（编译期确定、随Bean初始化注册）
85%UnverifiedToolContext不仅能获取自定义元数据，还能访问当前的聊天记录State，包含用户提示词、大模型响应、工具调用参数等完整对话上下文信息
85%