[Spring] Spring AI

Spring AI

Spring AI is an abstraction framework that supports the easy development of generative AI-based applications in the Java/Spring ecosystem. Early on, generative AI application development evolved primarily around the Python ecosystem, including LangChain, LlamaIndex, Python libraries from Neo4j, and Python libraries from various LLM providers. Numerous Python libraries quickly dominated the ecosystem, and the Java camp was relatively lagging behind.

Spring AI is a framework that helps easily integrate AI capabilities into Java/Spring applications based on the abstraction and auto-configuration that the Spring framework has pursued. It promotes portability, modular design, and a POJO-centric design as core principles.

The core value of Spring AI is not simply to follow the existing Python ecosystem, but to help existing Java/Spring developers develop generative AI-based applications in familiar ways and integrate them into existing Java/Spring-based web applications by integrating abstracted concepts necessary for AI applications into Spring Boot's auto-configuration and starters. It is the best option for integrating existing web applications with AI.

As of February 2026, version 1.1.2 has been released, and version 1.1.x is compatible with Spring Boot 3.5.x. Since it supports numerous model providers and vector stores based on the latest version, using Spring AI to integrate generative AI into existing web applications should not be a major problem. Especially in enterprise environments where strong dependencies on specific vendors can be an element to avoid, this abstraction can be a great advantage.

Key Features

Spring AI enables integration with various LLM model providers and vector stores based on existing abstracted interfaces. This allows developers to flexibly choose and change their technology stack without being tied to specific LLM models or vector stores. Basically, you can implement LLM model interactions, embedding processing, image generation, vector database linkage, and more by simply adding starter dependencies.

It provides a ChatClient interface, which is similarly named to WebClient and RestClient, which are widely known as existing REST API interfaces. The ChatClient interface is an abstracted interface that allows very basic interactions with AI and supports both synchronous and streaming calls. You can finely control and encapsulate workflows such as prompt pre-processing, RAG, memory injection, and response post-processing by integrating an Advisor chain. This structure can be seen as an implementation that closely follows the abstraction principles of existing Spring as an AI application framework.

Vector Search and RAG, widely known as popular features in LLM-based web applications, can also be easily implemented thanks to the abstraction of Spring AI. You can interact with a vector database through the VectorStore interface and embed data into vectors through the EmbeddingModel interface.

Features for agent implementation have also evolved significantly. Going beyond local function calls and remote tool calls based on the MCP protocol, it also provides the ability to orchestrate and parallelize one or more LLM calls to manage them as complex workflows. While it is true that it still lacks compared to LangGraph in terms of state management, it currently appears that agent execution state management, checkpoint management, and human-in-the-loop mechanisms are not yet supported. Managing the entire context of the interaction based on sessions, context, and memory should be sufficient in most cases. However, when an agent processes complex and numerous tasks, managing the state for intermediate steps is different from simply maintaining conversational context, and custom implementations may be required for this.

Abstraction Can Be a Disadvantage

Spring AI's powerful abstraction does not only offer advantages. Excessive abstraction complicates the implementation of the internal framework. In addition, you must carefully check if features specific to a certain provider have been integrated into the framework.

Since the support for various features (multimodal, function calling, structured JSON types, interruption via human intervention, etc.) may vary depending on the LLM provider, be careful when using them. The technical specifications of providers for generative AI are still lacking standardization, and the pace of technological development is very fast. Therefore, situations arise where you need to check whether the implementation of the desired feature is abstracted, or whether you need to use a specific provider for a custom implementation. Because if API exposure is limited by abstraction and compatibility issues remain, a custom implementation will eventually become necessary at some point.

[Spring] Spring AI

Spring AI

Key Features

Abstraction Can Be a Disadvantage

References