Spring AI
Java AI integration, prompt management abstractions, structured model output extraction, and RAG retrieval pipelines.
1 / Java-Native LLM Integration
Spring AI provided a Java-native abstraction for LLM integration within the Spring Boot ecosystem. In Conclave and Trajectory, it handled prompt management, structured model output extraction, and provider-agnostic model configuration.
2 / Multi-Provider Architecture
In Conclave, Spring AI's provider abstraction allowed switching between local Ollama instances and cloud API endpoints without changing the application's core orchestration logic. The framework handled the differences in API contracts, token counting, and response parsing across providers. This was valuable during development — testing with fast local models before deploying with cloud models — though the abstraction occasionally masked provider-specific behavior that affected output quality.
3 / Honest Assessment
Spring AI is still a relatively young framework compared to Python-based alternatives like LangChain or LangGraph. My experience with it is functional — I can build RAG retrieval pipelines and structured output extraction in Java — but for complex agent workflows with cyclic graphs and checkpoint persistence, the Python ecosystem remains significantly more mature.