Announcing the Google Gen AI SDK for Kotlin 1.0: Idiomatic multiplatform access to Gemini

Google has released version 1.0 of the Google Gen AI SDK for Kotlin, a Kotlin Multiplatform library providing idiomatic access to Gemini models for JVM and Android developers. The SDK, available on Maven Central as com.google.genai:google-genai-kotlin:1.0.0, supports unary and streaming text generation, chat, and works with both the Gemini Developer API and the Gemini Enterprise Agent Platform.

Integrating modern generative AI capabilities into Kotlin applications shouldn't require juggling raw HTTP clients or bridging disparate Java libraries. Today, we're excited to announce the 1.0 release of the Google Gen AI SDK for Kotlin google-genai-kotlin . You can dive right into the code, explore runnable samples, and star the project today on GitHub at https://github.com/googleapis/kotlin-genai googleapis/kotlin-genai . Built from the ground up as a Kotlin Multiplatform KMP library, the SDK brings idiomatic Kotlin paradigms including first-class Coroutines , asynchronous Flow streaming, and immutable data classes with named and default parameters to developers targeting both the JVM backend services, serverless functions, desktop and Android . The SDK provides a unified surface to interact with both the Gemini Developer API Google AI Studio and the Gemini Enterprise Agent Platform on Google Cloud with minimal configuration tweaks. 1. Getting started: Adding the dependency The SDK is published to Maven Central under com.google.genai:google-genai-kotlin . Kotlin Multiplatform KMP For multiplatform applications, add the dependency to your commonMain source set: - code block - <ListValue: StructValue 'code', '// build.gradle.kts\r\nkotlin {\r\n sourceSets {\r\n commonMain.dependencies {\r\n implementation "com.google.genai:google-genai-kotlin:1.0.0" \r\n }\r\n }\r\n}' , 'language', '' , 'caption', <wagtail.rich text.RichText object at 0x7fbaec563d00 Standard JVM or Android projects For single-platform Kotlin projects, Gradle automatically selects the optimal variant via Gradle Module Metadata: - code block - <ListValue: StructValue 'code', '// build.gradle.kts\r\ndependencies {\r\n implementation "com.google.genai:google-genai-kotlin:1.0.0" \r\n}' , 'language', '' , 'caption', <wagtail.rich text.RichText object at 0x7fbaec563670 2. Unary and streaming text generation and chat The primary entry point is the Client class. It manages HTTP connections and authentication automatically based on your environment variables GEMINI API KEY or GOOGLE API KEY for Google AI Studio, and GOOGLE GENAI USE ENTERPRISE=true with standard Google Cloud Application Default Credentials . Single prompt request with Gemini Flash Using Kotlin's use extension ensures the client's underlying network engine and HTTP connections are released cleanly: - code block - <ListValue: StructValue 'code', 'import com.google.genai.kotlin.Client\r\nimport kotlinx.coroutines.runBlocking\r\n\r\nfun main = runBlocking {\r\n Client .use { client - \r\n val response = client.models.generateContent \r\n model = "gemini-flash-latest",\r\n text = "Explain quantum entanglement in two sentences."\r\n \r\n\r\n println response.text \r\n }\r\n}' , 'language', '' , 'caption', <wagtail.rich text.RichText object at 0x7fbaec5631f0 Low-latency streaming with Coroutines Flow For interactive UIs and responsive CLI tools, generateContentStream returns a cold Kotlin Coroutine Flow<GenerateContentResponse , delivering token chunks in real time: - code block - <ListValue: StructValue 'code', 'import com.google.genai.kotlin.Client\r\nimport kotlinx.coroutines.runBlocking\r\n\r\nfun main = runBlocking {\r\n Client .use { client - \r\n val responseFlow = client.models.generateContentStream \r\n model = "gemini-flash-latest",\r\n text = "Outline the key architectural patterns for microservices on Google Cloud."\r\n \r\n\r\n responseFlow.collect { chunk - \r\n chunk.text?.let { print it }\r\n }\r\n println \r\n }\r\n}' , 'language', '' , 'caption', <wagtail.rich text.RichText object at 0x7fbaec563040 Multi-turn conversations chat Managing conversation history manually across request turns can become tedious. The SDK includes a dedicated chats service that automatically maintains context, appends turns, formats conversation history, and handles function calling: - code block - <ListValue: StructValue 'code', 'import com.google.genai.kotlin.Client\r\nimport com.google.genai.kotlin.types.Content\r\nimport com.google.genai.kotlin.types.GenerateContentConfig\r\nimport kotlinx.coroutines.runBlocking\r\n\r\nfun main = runBlocking {\r\n Client .use { client - \r\n val config = GenerateContentConfig \r\n systemInstruction = Content.fromText "You are an expert Google Cloud Solutions Architect." \r\n \r\n\r\n // Create a multi-turn chat session\r\n val chat = client.chats.create \r\n model = "gemini-flash-latest",\r\n config = config\r\n \r\n\r\n // Turn 1\r\n val firstResponse = chat.sendMessage "We are designing an event-driven ingestion pipeline on Google Cloud." \r\n println "Gemini: ${firstResponse.text}\\n" \r\n\r\n // Turn 2: context from the first turn is included automatically\r\n val secondResponse = chat.sendMessage "Which managed messaging service should we choose: Pub/Sub or Kafka?" \r\n println "Gemini: ${secondResponse.text}\\n" \r\n }\r\n}' , 'language', '' , 'caption', <wagtail.rich text.RichText object at 0x7fbaec563cd0 You can also use chat.sendMessageStream ... for streaming multi-turn chat responses. 3. Multimodal analysis grounded with Google Search Gemini's multimodal reasoning is especially effective when combined with external verification. For instance, when analyzing technical, medical, or scientific diagrams, you can attach Google Search Grounding to cross-check factual claims against live web sources. - code block - <ListValue: StructValue 'code', 'import com.google.genai.kotlin.Client\r\nimport com.google.genai.kotlin.types. \r\nimport java.io.File\r\nimport kotlinx.coroutines.runBlocking\r\n\r\nfun main = runBlocking {\r\n Client .use { client - \r\n val imageBytes = File "src/main/resources/medical diagram.png" .readBytes \r\n\r\n val content = Content \r\n parts = listOf \r\n Part inlineData = Blob mimeType = "image/png", data = imageBytes ,\r\n Part text = "Is this anatomical diagram accurate? Verify labels against authoritative medical sources." \r\n \r\n \r\n\r\n // Enable Google Search as a grounding tool\r\n val config = GenerateContentConfig \r\n tools = listOf Tool googleSearch = GoogleSearch \r\n \r\n\r\n val response = client.models.generateContent \r\n model = "gemini-flash-latest",\r\n content = content,\r\n config = config\r\n \r\n\r\n println "=== Analysis ===" \r\n println response.text \r\n\r\n // Inspect citations and search queries\r\n val grounding = response.groundingMetadata\r\n println "\\n=== Search Queries Executed ===" \r\n grounding?.webSearchQueries?.forEach { println "- $it" }\r\n\r\n println "\\n=== Grounding Sources ===" \r\n grounding?.groundingChunks?.mapNotNull { it.web }?.forEach { source - \r\n println "- ${source.title}: ${source.uri}" \r\n }\r\n }\r\n}' , 'language', '' , 'caption', <wagtail.rich text.RichText object at 0x7fbaec563ca0 4. Visual generation and conversational editing: The Gemini 3 image family The SDK provides full support for Google's latest image generation models popularly known as the Nano Banana series of models on leaderboards . Generating and Saving an Image Generated image bytes are delivered directly in the response parts as a Blob : - code block - <ListValue: StructValue 'code', 'import com.google.genai.kotlin.Client\r\nimport java.io.File\r\nimport kotlinx.coroutines.runBlocking\r\n\r\nfun main = runBlocking {\r\n Client .use { client - \r\n val response = client.models.generateContent \r\n model = "gemini-3.1-flash-image", // Nano Banana 2\r\n text = "A photorealistic blueprint of an eco-friendly modern datacenter, isometric view, 4k"\r\n \r\n\r\n val imagePart = response.parts?.firstOrNull { it.inlineData = null }\r\n imagePart?.inlineData?.data?.let { bytes - \r\n File "datacenter blueprint.png" .writeBytes bytes \r\n println "Image generated and saved successfully." \r\n }\r\n }\r\n}' , 'language', '' , 'caption', <wagtail.rich text.RichText object at 0x7fbaec5637f0 Conversational image-to-image editing You can pass existing images and conversational edit instructions in the same request: - code block - <ListValue: StructValue 'code', 'val originalImage = File "input.png" .readBytes \r\n\r\nval editPrompt = Content \r\n parts = listOf \r\n Part inlineData = Blob mimeType = "image/png", data = originalImage ,\r\n Part text = "Change the daylight illumination to a dramatic twilight skyline with illuminated windows." \r\n \r\n \r\n\r\nval editResponse = client.models.generateContent \r\n model = "gemini-3-pro-image", // Nano Banana Pro\r\n content = editPrompt\r\n ' , 'language', '' , 'caption', <wagtail.rich text.RichText object at 0x7fbaec563640 5. Real-time bidirectional interaction with Gemini Live For low-latency voice, audio, and live multimodal interactions, the SDK supports the Gemini Live API via persistent WebSocket connections using client.live.connect ... : - code block - <ListValue: StructValue 'code', 'import com.google.genai.kotlin.Client\r\nimport com.google.genai.kotlin.types.AudioTranscriptionConfig\r\nimport com.google.genai.kotlin.types.LiveConnectConfig\r\nimport kotlinx.coroutines.launch\r\nimport kotlinx.coroutines.runBlocking\r\n\r\nfun main = runBlocking {\r\n Client .use { client - \r\n val model = if client.enterprise "gemini-live-2.5-flash-native-audio"\r\n else "gemini-3.1-flash-live-preview"\r\n\r\n val config = LiveConnectConfig \r\n outputAudioTranscription = AudioTranscriptionConfig \r\n \r\n\r\n // Establish real-time bidirectional WebSocket session\r\n client.live.connect model, config .use { session - \r\n println "Connected to Gemini Live session " \r\n\r\n // Launch collector for server messages audio and text transcriptions \r\n val receiveJob = launch {\r\n session.receive .collect { serverMessage - \r\n serverMessage.serverContent?.outputTranscription?.text?.let { text - \r\n print text \r\n }\r\n }\r\n }\r\n\r\n // Stream real-time text or raw PCM audio blobs via session.sendRealtimeInput audio = ... \r\n session.sendRealtimeInput text = "Hello Gemini Give me a 5-second motivational quote." \r\n\r\n // When finished, clean up\r\n receiveJob.cancel \r\n session.closeSession \r\n }\r\n }\r\n}' , 'language', '' , 'caption', <wagtail.rich text.RichText object at 0x7fbaec563bb0 6. Structured tool and function calling When building agentic workflows or bridging LLMs with backend microservices, developers can pass structured JSON schemas via FunctionDeclaration . The model will intelligently select when to invoke the tool: - code block - <ListValue: StructValue 'code', 'val telemetryTool = FunctionDeclaration \r\n name = "getDatacenterMetrics",\r\n description = "Fetch real-time CPU and thermal telemetry for a Google Cloud region",\r\n parameters = Schema \r\n type = Type.OBJECT,\r\n properties = mapOf "region" to Schema type = Type.STRING ,\r\n required = listOf "region" \r\n \r\n \r\n\r\nval response = client.models.generateContent \r\n model = "gemini-flash-latest",\r\n text = "Check telemetry for europe-west1",\r\n config = GenerateContentConfig \r\n tools = listOf Tool functionDeclarations = listOf telemetryTool \r\n \r\n \r\n\r\nresponse.functionCalls?.firstOrNull ?.let { call - \r\n println "Model triggered tool: ${call.name} with arguments: ${call.args}" \r\n}' , 'language', '' , 'caption', <wagtail.rich text.RichText object at 0x7fbaed15ebb0 Additionally, when using the chats service, you can take advantage of Automatic Function Calling AFC , which means that functions declared in the chat conversation can be invoked automatically and transparently by the SDK on your behalf, as you can see in the following example: - code block - <ListValue: StructValue 'code', 'fun main = runBlocking {\r\n // A mocked function\r\n val getWeather = callableFunction "get weather", paramName = "city" { city: String - \r\n "18 degrees and sunny in $city"\r\n }\r\n\r\n Client .use { client - \r\n val chat = client.chats.create \r\n model = "gemini-flash-latest",\r\n automaticFunctionCalling = AutomaticFunctionCalling getWeather ,\r\n \r\n\r\n // SDK calls get weather if needed in this conversation\r\n println chat.sendMessage "What is the weather in Zurich?" .text \r\n }\r\n}' , 'language', '' , 'caption', <wagtail.rich text.RichText object at 0x7fbaed15ea30 What's next? With the 1.0 release of the Google Gen AI SDK for Kotlin, Kotlin developers across backend server ecosystems Ktor, Spring Boot, Quarkus, Micronaut and mobile applications now have a clean, multiplatform foundation for building generative AI applications. To learn more and get started, check out the following resources: We look forward to seeing what you build with Kotlin and Gemini