AI Development with Kotlin 2026

Introduction

Kotlin is used for Android, server-side, multiplatform. This guide shows you the best AI tools, SDKs, and patterns for Kotlin developers building AI-powered applications.

Top AI SDKs for Kotlin

Recommended: OpenAI Kotlin SDK, Spring AI

1. OpenAI Kotlin SDK

The OpenAI Kotlin SDK library is well-maintained and production-tested.

bash
Install
Use your Kotlin package manager
package: openai-kotlin-sdk

2. Spring AI

The Spring AI library is well-maintained and production-tested.

bash
Install
Use your Kotlin package manager
package: spring-ai

Quick Start

kotlin
// Kotlin AI quick start
// Import the appropriate SDK for Kotlin
// See OpenAI Kotlin SDK documentation for specific syntax
// 1. Initialize client with API key
// 2. Create a chat completion request
// 3. Handle the streaming or batch response
// Basic pattern (adapt to Kotlin syntax):
// client = new AIClient(apiKey: env["OPENAI_API_KEY"])
// response = client.chat(model: "gpt-4o-mini", message: "Hello!")

Kotlin-Specific Best Practices

Error Handling

typescript
import { RateLimitError } from 'openai';
async function safeAICall(message: string, maxRetries = 3): Promise {
  for (let i = 0; i < maxRetries; i++) {
    try {
      return await aiChat(message);
    } catch (error) {
      if (error instanceof RateLimitError && i < maxRetries - 1) {
        await new Promise(r => setTimeout(r, 1000 * Math.pow(2, i)));
      } else {
        throw error;
      }
    }
  }
  throw new Error('Max retries exceeded');
}

Streaming

typescript
// TypeScript streaming
async function* streamResponse(prompt: string): AsyncGenerator {
  const stream = await client.chat.completions.create({
    model: 'gpt-4o-mini',
    messages: [{ role: 'user', content: prompt }],
    stream: true
  });
  
  for await (const chunk of stream) {
    const content = chunk.choices[0]?.delta?.content;
    if (content) yield content;
  }
}
// Usage
for await (const token of streamResponse("Tell me about AI")) {
  process.stdout.write(token);
}

Structured Output

typescript
import { z } from 'zod';
const AnalysisSchema = z.object({
  summary: z.string(),
  keyPoints: z.array(z.string()),
  sentiment: z.enum(['positive', 'negative', 'neutral'])
});
type Analysis = z.infer;
async function analyze(text: string): Promise {
  const response = await client.chat.completions.create({
    model: 'gpt-4o',
    messages: [{
      role: 'user',
      content: Analyze: ${text}. Return JSON with summary, keyPoints array, sentiment.
    }],
    response_format: { type: 'json_object' }
  });
  
  const data = JSON.parse(response.choices[0].message.content || '{}');
  return AnalysisSchema.parse(data);
}

Real-World Kotlin AI Project

typescript
// Complete Kotlin AI application
import express from 'express';
import OpenAI from 'openai';
const app = express();
const openai = new OpenAI();
app.use(express.json());
app.post('/generate', async (req, res) => {
  const { prompt, model = 'gpt-4o-mini' } = req.body;
  
  const response = await openai.chat.completions.create({
    model,
    messages: [{ role: 'user', content: prompt }]
  });
  
  res.json({
    response: response.choices[0].message.content,
    model,
    tokens: response.usage?.total_tokens
  });
});
app.listen(3000);

Useful Libraries for Kotlin AI Development

OpenAI Kotlin SDK: Core AI SDK

LangChain.js: High-level AI orchestration

Pydantic (Zod for TS): Data validation for AI outputs

Instructor: Structured output from LLMs

RAGAS: Evaluate RAG system quality

Conclusion

Kotlin has an excellent ecosystem for AI development. With OpenAI Kotlin SDK, Spring AI, you can build everything from simple chatbots to complex AI agents.

The patterns in this guide are production-tested and will save you significant development time.

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*AI development with Kotlin | May 2026*