awesome-spring-ai

awesome-spring-ai

A curated list of awesome resources, tools, tutorials, and projects for building generative AI applications using Spring AI

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Awesome Spring AI is a curated list of resources, tools, tutorials, and projects for building generative AI applications using Spring AI. It provides a familiar developer experience for integrating Large Language Models and other AI capabilities into Spring applications, offering consistent abstractions, support for popular LLM providers, prompt engineering, caching mechanisms, vectorized storage integration, and more. The repository includes official resources, learning materials, code examples, community information, and tools for performance benchmarking.

README:

Awesome Spring AI Awesome

A curated list of awesome resources, tools, tutorials, and projects for building generative AI applications using Spring AI. This repository aims to help developers leverage the power of Large Language Models (LLMs) within the Spring ecosystem.

Contents

What is Spring AI?

Spring AI is a project from the Spring team that provides a familiar and consistent Spring-style developer experience for building AI applications. It simplifies the integration of Large Language Models and other AI capabilities into Spring applications, offering:

  • Consistent abstractions across different AI providers
  • Support for popular LLM providers
  • Robust prompt engineering capabilities
  • Built-in caching and retry mechanisms
  • Vectorized storage integration
  • Streaming responses
  • Customizable model parameters
  • Native Spring Boot integration

Official Resources

Documentation

Spring AI Blogs

Learning Resources

Books

Articles

Online Training

Talks & Videos

General Playlists

Workshops

Code & Examples

Comprehensive Example Collections

  • Spring AI Samples by Thomas Vitale - Extensive collection of samples showing how to build Java applications powered by Generative AI and Large Language Models (LLMs). Includes examples for different AI models, RAG implementations, and various Spring AI features.

  • Spring AI Examples by Craig Walls - Comprehensive repository with dozens of examples covering all major Spring AI capabilities, model integrations, and implementation patterns. Created by the author of "Spring AI in Action".

  • Spring AI Showcase by Piotr Minkowski - Modular demo project showcasing multiple Spring AI features including prompt templates, chat memory, structured output, function calling, RAG with Pinecone vector store, and image models. Supports multiple AI providers (OpenAI, Mistral, Ollama, Azure OpenAI) with profile-based configuration.

Code Examples

  • Spring PetClinic AI - The classic Spring PetClinic application enhanced with a chatbot powered by Spring AI. Demonstrates natural language interaction with application data, allowing users to query and modify pet clinic information through conversation. Supports both OpenAI and Azure OpenAI as LLM providers. Detailed in a two-part blog series on spring.io.

  • Flight Booking Assistant - Spring AI powered expert system demo that simulates a flight booking assistant. Demonstrates how to build domain-specific AI assistants using Spring AI.

  • Spring AI with QianFan - Spring AI support for various AI language models from QianFan. Shows how to interact with QianFan language models and create a multilingual conversational assistant based on QianFan models.

  • Similarity Search using Spring AI - Implementation of a simple similarity search. Demonstrating how to use Kotlin or Java with Spring-AI to generate embeddings and perform simple similarity searches (March 2025)

UI Clients

  • Spring AI HTMX MCP - Example of building a modern, interactive UI for Spring AI applications using HTMX. Demonstrates how to create a responsive chat interface with minimal JavaScript by leveraging HTMX's server-side rendering capabilities combined with Spring AI's Model Context Protocol.

  • Spring AI Vaadin - Integration of Spring AI with Vaadin, a Java web framework for building modern web applications. Provides components and examples for creating rich, interactive AI-powered UIs with pure Java, without requiring JavaScript or HTML knowledge.

  • DocumentGPT - A RAG-based document query system by Sergi Almar that allows users to upload documents and chat with them using Spring AI's vector search capabilities. Features a web-based user interface for document upload and interactive querying.

  • Spring AI Playground - A web UI designed to make it easy for Java developers to experiment with and integrate AI models. Provides an interactive interface for testing different prompts and models.

CLI Applications

  • Spring AI Chat Bot CLI - Command-line chatbot with Retrieval-Augmented Generation (RAG) and conversational memory capabilities. Demonstrates how to build interactive CLI applications with Spring AI.

  • Spring AI Powered Local CLI Chat Bot - A fully local, Spring AI-powered CLI chatbot that runs entirely on your machine with no external services required. Perfect for offline development or privacy-sensitive applications.

Extensions and Forks

  • Spring AI Alibaba - An extension of Spring AI that provides an agentic AI framework for Java developers. Adds support for Alibaba Cloud QWen models and Dashscope services, along with additional features like conversation memory, RAG support, and function calling. Maintains compatibility with the Spring AI API while offering specialized capabilities for Alibaba Cloud's AI ecosystem.

Development Tools

  • Arconia Ollama Dev Service - A Spring Boot development service that automatically manages Ollama instances for local LLM development. Simplifies testing and development with local models by handling container lifecycle and configuration. Integrates seamlessly with Spring AI's Ollama support.

Model Context Protocol

Core Resources

  • MCP Client Documentation - Official documentation for implementing the Model Context Protocol client in Spring AI applications.
  • MCP Client Examples - Comprehensive examples showcasing the Model Context Protocol implementation in Spring AI, including client-server communication, tool discovery, filesystem operations, weather services, web search integration, and dynamic tool updates.
  • MCP Annotations - Annotation-based programming model for implementing MCP servers and clients. Provides a clean, declarative approach to handling MCP operations with reduced boilerplate code. Includes core annotations that depend only on the MCP Java SDK and a Spring AI integration module.

MCP Servers for Spring Projects

  • Spring Batch MCP Server - An MCP service for introspecting Spring Batch applications, providing AI assistants with access to batch job information.
  • Spring Cloud Config MCP Server - An experimental MCP server implementation for Spring Cloud Config that exposes configuration management operations as AI tools, allowing AI assistants to retrieve, update, and refresh application configurations, as well as encrypt/decrypt sensitive values.
  • JVM Diagnostics MCP - A Model Context Protocol service for obtaining JVM diagnostics, allowing AI assistants to access runtime information about Java applications.

Domain-Specific MCP Implementations

  • Kotlin Crypto Price MCP Server - A Kotlin-based Spring AI MCP server that provides real-time cryptocurrency price information from Binance.
  • Spring AI MCP Database Integration Example - A practical implementation of MCP with Spring AI featuring two server applications exposing database operations (person and account data) via @Tool annotations and a client application that discovers and uses these tools with OpenAI models.
  • GitHub MCP Application - A 100% Java GitHub MCP application built on Spring AI by Stephan Janssen, creator of Devoxx.
  • AWS Sample MCP Demos - Collection of examples showing how to use Model Context Protocol with AWS services, including Spring AI implementations.

Community

Who to Follow

Podcasts

YouTube

Channels

Tools & Performance

Benchmarks

Contributing

Your contributions are always welcome! Please read the contribution guidelines first.

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