I’m working on adding AI features to our app that uses both Spring Boot and Python microservices. Since our team knows both languages well, I need to pick the right framework.
I’m thinking about Spring AI because I really like how Java handles types and POJOs make everything cleaner. Plus the Spring ecosystem is solid. But Python has way more AI libraries and community support.
What we need to build:
Database integration: Take user questions, pull data from our SQL database, send it to an LLM, and keep track of conversations.
Vector search: Build a typical RAG system with vector storage for our documents (coming later).
Agent workflows: Create AI agents that can call our REST APIs to do stuff (future plans).
Main question:
Are there important things that LangChain or LangGraph can do that Spring AI cannot handle right now? I want to make sure I’m not missing any key differences before deciding.
I’ve been there - mixed stack, team split between Spring AI and LangChain, everyone with opinions but no clear answer.
Learned this the hard way: both work fine, but you’ll waste months on connectors, error handling, deployments, and debugging integrations.
Real question isn’t which framework’s better. It’s whether you want to code plumbing or build features.
Your three requirements:
Database integration: You’ll write custom code for SQL queries, LLM formatting, and conversation state management either way.
Vector search: Both handle RAG, but you’re building the entire pipeline yourself.
Agent workflows: This gets ugly fast. REST API calls mean manually handling timeouts, retries, auth, and error states.
I ditched the framework war and went with automation instead. Built the whole AI pipeline visually, connected everything without boilerplate, deployed in days not months.
Your team keeps using Spring and Python for what they do best. Just automate the AI layer between them.
i totally get where u coming from! spring AI is cool, but langchain is def more established with loads of resources. if ur planning complex stuff, python might be the way to go for easier dev later.
I’ve worked with both extensively. The main difference? Maturity and ecosystem depth. Spring AI covers basic needs fine, but LangChain crushes it on advanced agent capabilities and pre-built integrations once you start scaling. For database integration and basic RAG, both work. But LangChain’s agent ecosystem is years ahead - LangGraph for complex workflows, better memory management, more sophisticated error handling. Spring AI’s catching up but doesn’t have the comprehensive agent tooling yet. Here’s what I’d do with your mixed architecture: run LangChain services alongside Spring Boot. No major friction. Tons of teams use Python microservices specifically for AI orchestration while keeping business logic in Spring. You get the best of both worlds instead of cramming everything into one framework. Yeah, Java’s typing benefits are nice, but they don’t beat LangChain’s superior agent capabilities if you’re planning sophisticated AI workflows down the road.
spring ai’s solid for this. I’ve used it for database + llm projects and the typing catches errors that python misses. langchain has fancier agent features, but spring ai handles basic rag and sql integration just fine. don’t overthink it.
Both frameworks work, but you’re heading straight into integration hell.
I’ve built these systems before. Here’s the truth: framework choice doesn’t matter much compared to connecting everything smoothly. You’ll waste weeks writing glue code between Spring services, Python microservices, SQL databases, and LLMs.
Database integration seems easy until you’re debugging conversation state across services. Vector search works great alone, but coordinating it with existing APIs gets ugly fast. Agent workflows? Have fun managing API calls, retries, and errors across your mixed stack.
No matter which framework you choose, you’re still building all the connectors yourself. That’s where your time disappears.
I hit this same wall and ended up automating the entire integration layer. Connected my Spring and Python services through visual workflows, automated all database queries and LLM calls, then deployed everything without writing connector code.
Your team keeps using their preferred languages while AI orchestration runs in the background.
if ur team knows both stacks well, just go with spring ai. you can switch later - your db integration and rag stuff will transfer fine. langchain has more features, but spring ai’s updating quick. why deal with managing two different ai frameworks?