Framework Integrations Overview
⚠️ This page is just unchecked AI-generated sample data to demonstrate the resource structure for flatmap-docs-kit.
Welcome to the comprehensive guide on integrating AI frameworks with Java applications. This section covers the most popular and powerful frameworks that enable Java developers to build sophisticated AI-powered applications.
🎯 What You'll Find Here
Our framework integrations section is organized around the most widely-used AI frameworks in the Java ecosystem:
LangChain4J
The Java port of the popular LangChain framework, providing a comprehensive toolkit for building LLM applications with chains, agents, and memory systems.
Spring AI
Spring's official AI integration framework, offering seamless integration with Spring Boot applications and providing abstractions for various AI providers.
MCP (Model Context Protocol)
A protocol for connecting AI models to external data sources and tools, enabling more powerful and context-aware AI applications.
🚀 Getting Started
Each framework section includes:
- Quick Start Guides - Get up and running in minutes
- Integration Examples - Real-world code samples
- Best Practices - Learn from the community
- Advanced Patterns - Scale your applications
🔧 Prerequisites
Before diving into these frameworks, make sure you have:
- Java 17+ installed
- Basic understanding of Java development
- Familiarity with Spring Boot (for Spring AI)
- An OpenAI API key or other LLM provider access
📚 Framework Comparison
| Framework | Best For | Learning Curve | Spring Integration |
|---|---|---|---|
| LangChain4J | Complex AI workflows | Moderate | Manual |
| Spring AI | Spring Boot apps | Low | Native |
| MCP | Tool integration | Advanced | Manual |
Choose the framework that best fits your project requirements and team expertise!