Spring AI
From Java Dev to AI Engineer: Spring AI Fast Track
What is Spring AI?
Spring AI is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to from java dev to ai engineer: spring ai fast track
From Java Dev to AI Engineer: Spring AI Fast Track
This server falls under the Coding Agents category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- From Java Dev to AI Engineer: Spring AI Fast Track
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx spring-aiConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Spring AI
The Spring AI repository is a comprehensive course resource hub that guides Java developers into AI engineering using the Spring AI framework. It covers building intelligent Spring Boot applications integrated with LLM providers like OpenAI, Ollama, and AWS Bedrock, including RAG pipelines backed by Qdrant vector databases. Developers use it to learn how to build production-grade AI features — prompt engineering, retrieval-augmented generation, and observability — without leaving the familiar Java ecosystem.
Prerequisites
- Java 17+ and Maven or Gradle installed
- Docker Desktop (for running Qdrant vector store and local Ollama models)
- An OpenAI API key (or access to AWS Bedrock / a local Ollama instance)
- Spring Boot 3.x project set up
- An MCP-compatible client such as Claude Desktop or Claude Code
Set up a Spring Boot project with Spring AI dependency
Create a new Spring Boot project and add the Spring AI BOM and the appropriate model starter (e.g., spring-ai-openai-spring-boot-starter) to your pom.xml. Spring AI's dependency management BOM ensures all module versions align.
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-openai-spring-boot-starter</artifactId>
</dependency>Configure your AI provider credentials
Add your API key and model settings to application.properties or application.yml. For OpenAI, set the key and default chat model. For Ollama, point the base URL at your local Docker container.
spring.ai.openai.api-key=${OPENAI_API_KEY}
spring.ai.openai.chat.options.model=gpt-4oStart a local vector store with Docker (for RAG)
Pull and run the Qdrant container so your Spring AI application can store and retrieve document embeddings for retrieval-augmented generation.
docker run -p 6333:6333 -p 6334:6334 qdrant/qdrantBuild and run your Spring AI application
Compile and start your application. Spring AI auto-configures the ChatClient, EmbeddingClient, and VectorStore beans based on your dependencies and properties.
./mvnw spring-boot:runAdd the MCP server configuration to your Claude client
Register the Spring AI MCP server in your Claude Desktop or Claude Code configuration so the AI assistant can invoke your Spring AI-backed tools.
{
"mcpServers": {
"spring-ai": {
"command": "npx",
"args": ["spring-ai"]
}
}
}Spring AI Examples
Client configuration
Minimal claude_desktop_config.json entry to connect Claude Desktop to the Spring AI MCP server.
{
"mcpServers": {
"spring-ai": {
"command": "npx",
"args": ["spring-ai"]
}
}
}Prompts to try
Example prompts once your Spring AI application is running and connected.
- "Explain how to configure a ChatClient bean in Spring AI with OpenAI"
- "Show me how to build a RAG pipeline in Spring Boot using Qdrant as the vector store"
- "How do I add observability to my Spring AI application with Micrometer and Grafana?"
- "Generate a Spring Boot service that answers questions from uploaded PDF documents"Troubleshooting Spring AI
Application fails to start with 'spring.ai.openai.api-key must not be blank'
Ensure the OPENAI_API_KEY environment variable is exported in your shell before running the app, or hardcode it temporarily in application.properties for local testing.
Qdrant connection refused when running RAG examples
Make sure the Qdrant Docker container is running and listening on port 6333. Run 'docker ps' to verify, then check spring.ai.vectorstore.qdrant.host and port settings in your application.properties.
MCP server command 'npx spring-ai' not found or errors on startup
The spring-ai npm package may not be published as a standalone MCP binary. Clone the eazybytes/spring-ai repository and follow the course setup instructions to build and run the server locally instead.
Frequently Asked Questions about Spring AI
What is Spring AI?
Spring AI is a Model Context Protocol (MCP) server that from java dev to ai engineer: spring ai fast track It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Spring AI?
Follow the installation instructions on the Spring AI GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Spring AI?
Spring AI works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Spring AI free to use?
Yes, Spring AI is open source and available under the MIT license. You can use it freely in both personal and commercial projects.
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