As you all know everyday new AI models are announced around the world and all individuals, companies try to leverage AI capabilities within their day to day work, projects and products. Spring AI is an organized solution to AI support within the Spring ecosystem.
Introduction Link to heading
In their website, Spring AI is defined as an application framework for AI engineering. Its goal is to apply to the AI domain Spring ecosystem design principles such as portability and modular design and promote using POJOs as the building blocks of an application to the AI domain. At its core, Spring AI addresses the fundamental challenge of AI integration: Connecting your enterprise Data and APIs with the AI Models.
It provides abstractions and tools for working with various AI models and providers, making it easier for developers to incorporate AI functionality without unnecessary complexity.
Key Features Link to heading
- Portable API support for chat, text-to-image, and embedding models across different AI providers.
- Support for major AI model providers like Anthropic, OpenAI, Microsoft, Amazon, Google, and Ollama.
- Structured output mapping of AI model responses to Java objects.
- Integration with vector databases for efficient data storage and retrieval.
- Tools and function calling capabilities.
- Spring Boot auto-configuration and starters for AI models and vector stores.
- ChatClient API for fluent communication with AI chat models.
- Support for chat conversation memory and Retrieval Augmented Generation (RAG)
Comparison with LangChain4j Link to heading
There are other alternatives within the Java ecosystem like LangChain4j. LangChain4j is a Java framework designed to simplify the development of LLM/RAG applications based on LangChain. It provides abstractions and tools for working with various LLM providers and implementing RAG workflows. But in these series we’ll focus on the Spring AI within the Spring ecosystem.
If you’re not clear deciding between Spring AI and LangChain4j, you can consider following factors when choosing one:
- Integration with existing technology stack: Spring AI integrates seamlessly with the Spring ecosystem, making it an ideal choice for teams already using Spring.
- Development stage: As of March 2025, Spring AI is still in pre-1.0 release (version 0.8.0 snapshot), while LangChain4j may be more mature in some aspects.
- Feature set: Compare the specific features and integrations offered by each framework to ensure they meet your project requirements.
- Community support and documentation: Spring AI benefits from the extensive Spring ecosystem and documentation, while other frameworks may have different levels of community support.
- Long-term viability: Consider the backing and support of each framework. Spring AI is developed by the Spring team, which may provide more confidence in its long-term support.
Ultimately, the choice between Spring AI and LangChain4j will depend on your specific project needs, existing technology stack, and development preferences.
Ecosystem Link to heading
I also found the Spring ecosystem very active and helpful. Dan Vega, a helpful Spring Developer Advocate, creates blog posts and Youtube videos about usage of different components within Spring AI. I encourage you to review Awesome Spring AI for up-to-date, curated list of resources.
As I’m also a beginner to Spring AI, I’ll try to explore the features of Spring AI with this series and share my learnings with you.