The Model Context Protocol (MCP) is a powerful feature in Spring AI that enables AI models to access external tools and resources through a standardized interface. One interesting capabilities of MCP is its ability to dynamically update available tools at runtime.

This blog post explores how Spring AI implements dynamic tool updates in MCP, providing flexibility and extensibility to AI-powered applications.

The related example code is available here: Dynamic Tool Update Example

Understanding the Model Context Protocol

Before diving into dynamic tool updates, let's understand what MCP is and…

This blog post demonstrates practical implementations of Prompt Engineering techniques using Spring AI.

The examples and patterns in this article are based on the comprehensive Prompt Engineering Guide that covers the theory, principles, and patterns of effective prompt engineering.

The blog shows how to translate those concepts into working Java code using Spring AI's fluent ChatClient API.

For convenience, the examples are structured to follow the same patterns and techniques outlined in the original guide.

The demo source code used in this article is available at: https://github.com/spring…

In a recent research publication: Building effective agents, Anthropic shared valuable insights about building effective Large Language Model (LLM) agents. What makes this research particularly interesting is its emphasis on simplicity and composability over complex frameworks. Let's explore how these principles translate into practical implementations using Spring AI.

While the pattern descriptions and diagrams are sourced from Anthropic's original publication, we'll focus on how to implement these patterns using Spring AI's features for model portability and structured output. We recommend…

We're excited to introduce Spring AI MCP, a robust Java SDK implementation of the Model Context Protocol (MCP). This new addition to the Spring AI ecosystem brings standardized AI model integration capabilities to the Java platform.

What is MCP?

The Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to Large Language Models (LLMs). MCP provides a standardized way to connect AI models to different data sources and tools, making integration seamless and consistent. It helps you build agents and complex workflows on top of LLMs. LLMs frequently…

The Amazon Bedrock Nova models represent a new generation of foundation models supporting a broad range of use cases, from text and image understanding to video-to-text analysis.

With the Spring AI Bedrock Converse API integration, developers can seamlessly connect to these advanced Nova models and build sophisticated conversational applications with minimal effort.

This blog post introduces the key features of Amazon Nova models, demonstrates their integration with Spring AI's Bedrock Converse API, and provides practical examples for text, image, video, document processing, and function…

This blog post is co-authored by our great contributor Thomas Vitale.

OpenAI provides specialized models for speech-to-text and text-to-speech conversion, recognized for their performance and cost-efficiency. Spring AI integrates these capabilities via Voice-to-Text and Text-to-Speech (TTS).

The new Audio Generation feature (gpt-4o-audio-preview) goes further, enabling mixed input and output modalities. Audio inputs can contain richer data than text alone. Audio can convey nuanced information like tone and inflection, and together with the audio outputs it enables asynchronous speech-to-speech interactions. Additionally, this new multimodality opens up possibilities for innovative applications, such as structured data extraction. Developers can extract structured information not just from simple text, but also from images and audio, building complex, structured objects…

This blog post is co-authored by our great contributor Thomas Vitale.

Ollama now supports all GGUF models from Hugging Face, allowing access to over 45,000 community-created models through Spring AI's Ollama integration, runnable locally.

We'll explore using this new feature with Spring AI. The Spring AI Ollama integration can automatically pull unavailable models for both chat completion and embedding models. This is useful when switching models or deploying to new environments.

Setting Up Spring AI with Ollama

Install Ollama on your system: https://ollama.com/download.

Tip: Spring AI also supports running Ollama via Testcontainers or integrating with an external Ollama service via Kubernetes Service Bindings…

In the rapidly evolving world of artificial intelligence, developers are constantly seeking ways to enhance their AI applications. Spring AI, a Java framework for building AI-powered applications, has introduced a powerful feature: the Spring AI Advisors.

The advisors can supercharge your AI applications, making them more modular, portable and easier to maintain.

If reading the post isn't convenient, you can listen to this experimental podcast, AI-generated from blog's content:

What are Spring AI Advisors?

At their core, Spring AI Advisors are components that intercept and potentially modify the flow of chat-completion requests and responses in your AI applications. The key player in this system is the AroundAdvisor…

Spring AI now supports NVIDIA's Large Language Model API, offering integration with a wide range of models. By leveraging NVIDIA's OpenAI-compatible API, Spring AI allows developers to use NVIDIA's LLMs through the familiar Spring AI API.

We'll explore how to configure and use the Spring AI OpenAI chat client to connect with NVIDIA LLM API.

• The demo application code is available in the nvidia-llm GitHub repository.
• The SpringAI / NVIDIA integration documentation.

Prerequisite

• Create NVIDIA account with sufficient credits.
• Select your preferred LLM model from NVIDIA's offerings. Like the meta/llama-3.1-70b-instruct in the screenshot below.
• From the model's page, obtain the API key for your chosen model.
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OpenAI recently introduced a powerful feature called Structured Outputs, which ensures that AI-generated responses adhere strictly to a predefined JSON schema. This feature significantly improves the reliability and usability of AI-generated content in real-world applications. Today, we're excited to announce that Spring AI (1.0.0-SNAPSHOT) has fully integrated support for OpenAI's Structured Outputs, bringing this capability to Java developers in a seamless, Spring-native way.

Following diagram shows how the new Structured Outputs feature extends the OpenAI Chat API:

Note: Spring AI already provides a powerful, Model-agnostic Structured Output utilities that can be use with various AI models including the OpenAI. The OpenAI Structured Outputs feature offers an additional, consistent, but a model specific solution, currently available only to the gpt-4o, gpt-4o-mini and later models.

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