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A collection of Kotlin-based examples featuring AI frameworks such as Spring AI, LangChain4j, and more — complete with Kotlin notebooks for hands-on learning.
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Kotlin AI Examples is a repository showcasing various AI frameworks integrated into Kotlin-based projects.
Here you’ll find ready-to-use examples for Spring AI, LangChain4j, as well as interactive Kotlin notebooks.
/projects)This section contains complete Kotlin projects demonstrating AI integrations.
/projects/spring-ai)/projects/langchain4j)/projects/mcp)/notebooks)A collection of interactive Kotlin notebooks, organized by project.
[!TIP] For detailed descriptions, getting started guide, and tutorials, see the Kotlin AI Notebooks README.
/notebooks/spring-ai)SpringAI_Overview.ipynb: overview of Spring AI with Kotlin
Tutorial Series This series of interactive notebooks provides a hands-on journey through Spring AI capabilities with Kotlin. Each notebook builds on the previous one, guiding you from basic concepts to advanced AI integrations.
| Notebook | Description |
|---|---|
| 1. Intro | Introduction to Spring AI fundamentals and setup with Kotlin |
| 2. Prompts | Working with prompt engineering techniques and best practices |
| 3. Streaming | Implementing streaming responses for real-time AI interactions |
| 4. Tools | Connecting LLMs to external tools and functions for enhanced capabilities |
| 5. Structured Outputs | Getting formatted, structured data from LLMs for easier application integration |
| 6. Advisors | Using Spring AI's Advisor system to enhance and customize AI interactions |
| 7. RAG | Implementing Retrieval-Augmented Generation with your own data sources |
| 8. Text-to-Image | Converting text descriptions into generated images with Spring AI |
| 9. Text-to-Audio | Creating speech from text and transcribing audio with AI models |
/notebooks/agents)| Notebook | Description |
|---|---|
| Prompt Chaining | Implements a sequential workflow that breaks complex tasks into simpler steps, where each LLM call processes the output of the previous one |
| Routing | Shows how to classify inputs and direct them to specialized handlers, optimizing for different types of requests |
| Parallelization | Demonstrates running multiple LLM tasks simultaneously using Kotlin coroutines for improved performance |
| Orchestrator-Workers | Implements a pattern where a central LLM breaks down tasks dynamically and delegates them to specialized worker LLMs |
| Evaluator-Optimizer | Creates a feedback loop where one LLM generates responses and another evaluates them for continuous improvement |
/notebooks/langchain4j)/notebooks/openai)/notebooks/arc)/notebooks/kInference)/notebooks/xefAI)git clone https://github.com/Kotlin/Kotlin-AI-Examples.git
projects/ has its own README with setup and run instructions.ipynb file[!NOTE] Make sure you have the necessary access credentials for your chosen AI service (OpenAI, Azure, VertexAI, etc.) and the required dependencies in your build scripts (Gradle/Maven).
This project is licensed under the Apache License 2.0.
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