Spring AI in Action: Building Real AI Apps with Spring Boot

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Spring AI in Action is quickly becoming the go‑to reference for Java and Spring Boot developers who want to bring modern generative AI into their everyday projects without abandoning the JVM stack. Instead of forcing you into Python ecosystems or obscure tooling, the book and the framework work hand in hand so you can keep coding in Java or Kotlin while still integrating powerful Large Language Models (LLMs), Retrieval Augmented Generation (RAG), agents, tools and multimodal features.

What makes this ecosystem so appealing is the combination of a production‑ready framework (Spring AI) and a highly pragmatic, example‑driven guide (Spring AI in Action by Craig Walls). Together they show how to wire AI models, vector databases, conversational memory and evaluation tools into familiar Spring Boot apps using simple POJOs, auto‑configuration and a clean, portable API that hides a lot of provider‑specific complexity.

What is Spring AI and why it matters for Java developers

Spring AI is an application framework designed to bring the classic Spring principles—portability, modular architecture and POJO‑centric design—into the AI engineering world. At its heart, Spring AI focuses on solving the hardest practical problem in enterprise AI: connecting your organization’s data and APIs with modern AI models in a way that is maintainable, observable and easy to evolve over time.

Instead of locking you into a single LLM vendor, Spring AI abstracts over most of the big providers. Out of the box, you can talk to models from OpenAI, Azure OpenAI, Anthropic, Amazon Bedrock, Google, MistralAI and even local models served via Ollama. The same programming model supports both synchronous and streaming responses, and you still retain access to provider‑specific capabilities when you really need them.

Another key pillar of Spring AI is its strong support for structured output. Rather than parsing raw text by hand, you can map model responses directly to Java classes and records, turning messy natural language into clean POJOs. This is essential when you build agents, tools or workflows that must reason over predictable data instead of unstructured text.

Spring AI also integrates deeply with vector databases so you can implement Retrieval Augmented Generation without reinventing the wheel. It supports providers like Apache Cassandra, Azure Vector Search, Chroma, Milvus, MongoDB Atlas, Neo4j, Oracle integration, PostgreSQL with PGVector, Pinecone, Qdrant, Redis and Weaviate. A portable Vector Store API and a SQL‑like metadata filter language let you change vector backends with minimal code changes.

On top of all that, Spring AI ships with tooling for observability, document ingestion pipelines, model evaluation and generative AI patterns. You get a fluent ChatClient similar to WebClient/RestClient, Advisors for common AI patterns like RAG and conversational memory, auto‑configuration with Spring Boot starters, and utilities for monitoring token usage and detecting hallucinations.

Inside “Spring AI in Action”: from Hello AI World to full agents

“Spring AI in Action” by Craig Walls is the practical, hands‑on guide that shows you how to use all of these Spring AI capabilities in real applications. The book is aimed squarely at Spring developers and assumes you already know Spring Boot, but it does not require prior generative AI experience; you don’t have to be a data scientist or “AI expert” to follow along.

The journey in the book starts with a simple “Hello AI World” example and gradually introduces more advanced techniques as you get comfortable. You begin by wiring a basic LLM call inside a Spring Boot app, then move on to generating text summaries, building assistants that live inside your existing web or backend services, and shaping prompts so that responses are more helpful and predictable.

As you progress, the content digs into RAG, vector stores and multimodal scenarios where models work with both text and images. You learn how to ask questions about private documents the model was never trained on, how to turn images into text and vice versa, and how to ground LLM answers in your own data so they stop hallucinating when facing domain‑specific questions.

The second half of the book raises the bar by exploring agents, tool use, speech, and observability. Here you see how to build AI agents that can decide when to call tools or APIs, how to route tasks to specialized prompts, how to track what’s happening through metrics and traces, and how to keep your system safe with evaluation and safeguards around generated content.

Throughout the book, Craig Walls keeps his trademark, example‑driven style, always focusing on “getting stuff done” instead of drowning you in theory. Chapters are full of pragmatic snippets and realistic scenarios: chatbots that actually know your data, assistants embedded in business workflows, and agents that decompose complex tasks into smaller, manageable pieces.

Key topics and structure of the book

The table of contents of “Spring AI in Action” gives a clear picture of the breadth of what you will build. From foundational building blocks to advanced patterns, each chapter focuses on a specific area of AI integration with Spring:

• Getting started with Spring AI: bootstrapping a project, configuring providers, sending your first prompts.
• Evaluating generated responses: measuring quality, detecting issues, and protecting against low‑quality or hallucinated content.
• Submitting prompts for generation: designing prompts, using templates and controlling model behavior.
• Talking with your documents: implementing RAG so LLMs can answer questions about untrained, private data.
• Enabling conversational memory: maintaining multi‑turn chat context using Spring AI’s memory advisors.
• Activating tool‑driven generation: letting models call client‑side functions and tools when they need fresh or external data.
• Applying Model Context Protocol (MCP): managing richer context and interactions with tools and data sources.
• Generating with voice and pictures: embracing multimodal capabilities for speech and images.
• Observing AI operations: adding observability and monitoring into your AI pipelines.
• Safeguarding generative AI: applying guardrails, content filters and other protection mechanisms.
• Applying generative AI patterns: capturing reusable patterns for AI workflows.
• Employing agents: constructing agentic systems that can plan, route and refine work.

Reviews from respected voices in the Spring and Java communities highlight how accessible and practical the material is. Foreword authors and reviewers praise the book for clear explanations, extensive demos and “treasure trove” depth on emerging technologies, underlining that it remains grounded in real‑world development rather than academic abstraction.

When you buy the print edition from Manning you also get a free eBook (PDF or ePub) plus access to their online liveBook version. The liveBook platform itself includes an AI assistant capable of answering your questions in multiple languages, so you can explore examples, search through the text and clarify topics while you read.

Spring AI core features for enterprise‑grade AI apps

Beyond the book, the Spring AI framework exposes a comprehensive feature set tailored to production‑grade AI applications. It is not just about calling an LLM; it is about building complete systems that are secure, observable, testable and portable across providers and environments.

The same level of flexibility extends to vector stores. With support for Apache Cassandra, Azure Vector Search, Chroma, Milvus, MongoDB Atlas, Neo4j, Oracle, PostgreSQL/PGVector, Pinecone, Qdrant, Redis, Weaviate and others, you can implement RAG and semantic search without hard‑wiring your app to a single storage solution. A portable API and expressive metadata filters make it easier to run complex similarity queries.

Tools and function calling are first‑class citizens in Spring AI. Models can request the execution of client‑side tools and functions to retrieve real‑time data or trigger actions. This turns your LLM from a passive text generator into an active component that can query APIs, call databases or orchestrate services through typed function calls.

Observability is baked into the framework so you can see what your AI is doing under the hood. You can collect metrics on token usage, latency and error rates, trace calls through your system and correlate LLM activity with the rest of your microservices. This is critical when AI moves from experiments to business‑critical workloads.

Spring AI also includes an ETL‑style document ingestion framework for data engineering tasks. It helps you load, chunk and index documents into vector stores so your RAG pipelines are robust and repeatable, rather than a collection of ad‑hoc scripts.

ChatClient, Advisors and conversational capabilities

At the coding level, most Spring AI interactions revolve around the ChatClient API, a fluent interface inspired by familiar Spring WebClient and RestClient patterns. You build and send prompts, receive responses, stream tokens as they arrive and handle errors in a way that feels immediately natural to Spring developers.

Advisors are another key abstraction that encapsulate common generative AI patterns. They transform the data going to and coming from LLMs, layer on behaviors like RAG or memory, and provide portability across models and use cases. Instead of hand‑wiring every prompt or context, you plug in Advisors to get robust behavior with minimal boilerplate.

Conversational memory is handled through specialized chat memory advisors that manage multi‑turn dialog. Since LLMs themselves are stateless and “forget” past turns, these advisors track conversation history and feed the right slices of context back into each prompt. You can choose among different strategies and even implement persistent, long‑term memory with vector‑based approaches.

The combination of chat memory and RAG Advisors allows you to build assistants that can “talk” to your documents over multiple turns. A user can ask follow‑ups, refine their questions and reference earlier parts of the conversation, while Spring AI automatically retrieves and injects the most relevant document snippets on every request.

Prompt templates make it easy to externalize and reuse prompts. You define generic templates that accept parameters, include additional instructions and specify the desired output format (for example JSON that maps directly to Java objects). Before the prompt is sent, Spring AI fills in the blanks, applies context and ensures the instructions are clear to the model.

RAG, hallucination reduction and document‑aware assistants

Retrieval Augmented Generation (RAG) is one of the most important patterns covered by both the framework and the book. It solves a critical limitation of static LLMs: they only know what they were trained on, which means they cannot see your internal documentation, customer data or proprietary knowledge by default.

With RAG, your application first retrieves a small set of documents that are semantically similar to the user’s question and then feeds those into the model as context. Spring AI abstracts a lot of this work, integrating with dozens of vector stores and providing an API to query by similarity, filter by metadata and tune the way you chunk and embed your content.

Properly implemented RAG dramatically reduces hallucinations. Instead of guessing when it lacks information or is over‑trained on generic internet data, the model is steered toward high‑quality, domain‑specific snippets. The book walks through “chat with your documentation” and “Q&A over your docs” use cases that show this pattern end to end.

Through QuestionAnswerAdvisor and ChatClient, you can either drive the whole RAG flow explicitly or let the Advisor orchestrate embedding, retrieval and context injection for you. That gives you flexibility: start with the simple approach to move fast, then drop down a level when you need custom behavior or deep optimization.

Because Spring AI supports streaming responses, those document‑aware answers can be streamed back to a UI as they are generated. This mimics a human typing in real time and provides better user experience, especially when answers are long or model latency is high.

Agentic patterns inspired by Anthropic research

Spring AI also implements a set of agentic patterns inspired by Anthropic’s research on building effective LLM agents. The emphasis is on simplicity and composability rather than heavyweight, opaque agent frameworks, which aligns well with enterprise requirements for maintainable, testable systems.

The first pattern, the Chain Workflow, breaks big tasks into a series of smaller, ordered steps. Each step uses its own prompt, consumes the previous step’s output and produces refined intermediate results. In Spring AI, this looks like iterating over system prompts and invoking ChatClient repeatedly, passing the previous response as part of the next input, creating a clear and extendable pipeline.

Parallelization Workflow is about running multiple LLM calls at the same time and aggregating their outputs. You can use it for “sectioning” (splitting work into independent chunks) or “voting” (having several model runs tackle the same prompt and then combining results). For example, you might ask the model to analyze the impact of market changes on customers, employees, investors and suppliers in parallel, then merge those insights.

The Routing Workflow introduces intelligent dispatching to the mix. An LLM first classifies the input and decides which specialized prompt or handler should process it: billing questions go to one expert prompt, technical issues to another, generic queries to a general helper. Spring AI’s routing workflow ties this logic together through ChatClient and a map of routes.

Orchestrator‑Workers is a more advanced pattern that still avoids uncontrolled autonomy. A central “orchestrator” model decomposes a complex task into subtasks, then specialized workers tackle those subtasks, often in parallel. Once workers finish, their outputs are merged into a final result. Spring AI provides the building blocks to implement this pattern while keeping responsibilities clear and predictable.

Finally, the Evaluator‑Optimizer pattern uses two cooperating models. One model acts as a generator that proposes solutions, while a second model behaves like a critic or reviewer, checking the solution against clear criteria and feeding back improvements. This loop continues until the evaluator is satisfied, producing a refined response along with a trail of the solution’s evolution.

Best practices, reliability and future evolution

The patterns and features in Spring AI are accompanied by clear best practices that emerge both from Anthropic’s research and the Spring ecosystem’s production experience. The common advice is to start with the simplest workflow that can possibly work, then layer complexity only when it demonstrably adds value.

Reliability should be a first‑class concern in any LLM‑enabled system. That means using type‑safe structured output wherever possible, validating responses, adding strong error handling and retries, and instrumenting your pipelines with metrics and logs. When something goes wrong, you should be able to understand why and fix it quickly.

Developers are encouraged to carefully weigh latency versus accuracy trade‑offs. Chaining multiple steps or adding evaluator loops can significantly improve quality but will also increase response times and token consumption. Parallelization can help regain speed, but only when tasks are truly independent.

Future work in the Spring AI ecosystem will deepen capabilities around pattern composition, advanced memory strategies and tool integration. Composing multiple patterns—like chaining, routing and evaluator loops—lets you build sophisticated agents that still remain understandable. Advanced memory management explores persistent context, efficient context windows and long‑term knowledge retention.

Tools and Model Context Protocol (MCP) integration are another active area. Standardized interfaces for external tools and a richer protocol for model context mean agents can safely and flexibly reach into your services, APIs and data sources, all under your governance and observability stack.

Spring AI in the wider platform: Tanzu Gen AI Solutions

For organizations building on VMware’s Tanzu stack, Spring AI also underpins Tanzu Gen AI Solutions. Tanzu AI Server, powered by Spring AI, offers a production‑ready environment for deploying AI applications on Tanzu Platform with enterprise‑grade security, governance and scalability.

This integration simplifies access to models such as Amazon Bedrock Nova through a unified interface. Instead of each team wiring its own model connections, the platform standardizes access, security policies and operational tooling. Spring AI handles model portability, while Tanzu provides the robust infrastructure, autoscaling and observability you expect from a modern Kubernetes platform.

Because Spring AI is responsible for the application‑level abstraction, teams can move between providers or adopt new models without rewriting their business logic. That adaptability is crucial in a fast‑moving AI landscape where new models appear frequently and pricing or capabilities can change rapidly.

Security and governance features in Tanzu Gen AI Solutions wrap these AI applications in the same enterprise controls used for other microservices. Policies, access control, audit trails and compliance tooling extend naturally to LLM workloads, making it more feasible to run sensitive or regulated use cases.

All these layers—framework, book, patterns and platform—converge toward the same goal: enabling Spring developers to add high‑value AI features like virtual assistants, smart search, text summarization and recommendations directly into Java applications without sacrificing reliability or control. With Spring AI in Action as your practical guide and Spring AI as your engineering backbone, you can move from experiments to robust AI‑powered services while staying within the Spring ecosystem you already know so well.