Build multi-turn conversations (chat) using the Gemini API

Using the Gemini API, you can build freeform conversations across multiple turns. The Firebase AI Logic SDK simplifies the process by managing the state of the conversation, so unlike with generateContent() (or generateContentStream()), you don't have to store the conversation history yourself.

Jump to code for text-only chat Jump to code for iterative image editing Jump to code for streamed responses

Before you begin

Click your Gemini API provider to view provider-specific content and code on this page.

If you haven't already, complete the getting started guide, which describes how to set up your Firebase project, connect your app to Firebase, add the SDK, initialize the backend service for your chosen Gemini API provider, and create a GenerativeModel instance.

For testing and iterating on your prompts, we recommend using Google AI Studio.

Build a text-only chat experience

Before trying this sample, complete the Before you begin section of this guide to set up your project and app.
In that section, you'll also click a button for your chosen Gemini API provider so that you see provider-specific content on this page.

To build a multi-turn conversation (like chat), start off by initializing the chat by calling startChat(). Then use sendMessage() to send a new user message, which will also append the message and the response to the chat history.

There are two possible options for role associated with the content in a conversation:

  • user: the role which provides the prompts. This value is the default for calls to sendMessage(), and the function throws an exception if a different role is passed.

  • model: the role which provides the responses. This role can be used when calling startChat() with existing history.

Swift

You can call startChat() and sendMessage() to send a new user message:

 import FirebaseAI // Initialize the Gemini Developer API backend service let ai = FirebaseAI.firebaseAI(backend: .googleAI()) // Create a `GenerativeModel` instance with a model that supports your use case let model = ai.generativeModel(modelName: "gemini-2.5-flash") // Optionally specify existing chat history let history = [  ModelContent(role: "user", parts: "Hello, I have 2 dogs in my house."),  ModelContent(role: "model", parts: "Great to meet you. What would you like to know?"), ] // Initialize the chat with optional chat history let chat = model.startChat(history: history) // To generate text output, call sendMessage and pass in the message let response = try await chat.sendMessage("How many paws are in my house?") print(response.text ?? "No text in response.")

Kotlin

You can call startChat() and sendMessage() to send a new user message:

For Kotlin, the methods in this SDK are suspend functions and need to be called from a Coroutine scope. 
 // Initialize the Gemini Developer API backend service // Create a `GenerativeModel` instance with a model that supports your use case val model = Firebase.ai(backend = GenerativeBackend.googleAI())  .generativeModel("gemini-2.5-flash") // Initialize the chat val chat = model.startChat(  history = listOf(  content(role = "user") { text("Hello, I have 2 dogs in my house.") },  content(role = "model") { text("Great to meet you. What would you like to know?") }  ) ) val response = chat.sendMessage("How many paws are in my house?") print(response.text)

Java

You can call startChat() and sendMessage() to send a new user message:

For Java, the methods in this SDK return a ListenableFuture. 
 // Initialize the Gemini Developer API backend service // Create a `GenerativeModel` instance with a model that supports your use case GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI())  .generativeModel("gemini-2.5-flash"); // Use the GenerativeModelFutures Java compatibility layer which offers // support for ListenableFuture and Publisher APIs GenerativeModelFutures model = GenerativeModelFutures.from(ai); // (optional) Create previous chat history for context Content.Builder userContentBuilder = new Content.Builder(); userContentBuilder.setRole("user"); userContentBuilder.addText("Hello, I have 2 dogs in my house."); Content userContent = userContentBuilder.build(); Content.Builder modelContentBuilder = new Content.Builder(); modelContentBuilder.setRole("model"); modelContentBuilder.addText("Great to meet you. What would you like to know?"); Content modelContent = userContentBuilder.build(); List<Content> history = Arrays.asList(userContent, modelContent); // Initialize the chat ChatFutures chat = model.startChat(history); // Create a new user message Content.Builder messageBuilder = new Content.Builder(); messageBuilder.setRole("user"); messageBuilder.addText("How many paws are in my house?"); Content message = messageBuilder.build(); // Send the message ListenableFuture<GenerateContentResponse> response = chat.sendMessage(message); Futures.addCallback(response, new FutureCallback<GenerateContentResponse>() {  @Override  public void onSuccess(GenerateContentResponse result) {  String resultText = result.getText();  System.out.println(resultText);  }  @Override  public void onFailure(Throwable t) {  t.printStackTrace();  } }, executor);

Web

You can call startChat() and sendMessage() to send a new user message:

 import { initializeApp } from "firebase/app"; import { getAI, getGenerativeModel, GoogleAIBackend } from "firebase/ai"; // TODO(developer) Replace the following with your app's Firebase configuration // See: https://firebase.google.com/docs/web/learn-more#config-object const firebaseConfig = {  // ... }; // Initialize FirebaseApp const firebaseApp = initializeApp(firebaseConfig); // Initialize the Gemini Developer API backend service const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() }); // Create a `GenerativeModel` instance with a model that supports your use case const model = getGenerativeModel(ai, { model: "gemini-2.5-flash" }); async function run() {  const chat = model.startChat({  history: [  {  role: "user",  parts: [{ text: "Hello, I have 2 dogs in my house." }],  },  {  role: "model",  parts: [{ text: "Great to meet you. What would you like to know?" }],  },  ],  generationConfig: {  maxOutputTokens: 100,  },  });  const msg = "How many paws are in my house?";  const result = await chat.sendMessage(msg);  const text = result.response.text();  console.log(text); } run();

Dart

You can call startChat() and sendMessage() to send a new user message:

 import 'package:firebase_ai/firebase_ai.dart'; import 'package:firebase_core/firebase_core.dart'; import 'firebase_options.dart'; // Initialize FirebaseApp await Firebase.initializeApp(  options: DefaultFirebaseOptions.currentPlatform, ); // Initialize the Gemini Developer API backend service // Create a `GenerativeModel` instance with a model that supports your use case final model =  FirebaseAI.googleAI().generativeModel(model: 'gemini-2.5-flash'); final chat = model.startChat(); // Provide a prompt that contains text final prompt = [Content.text('Write a story about a magic backpack.')]; final response = await chat.sendMessage(prompt); print(response.text);

Unity

You can call StartChat() and SendMessageAsync() to send a new user message:

 using Firebase; using Firebase.AI; // Initialize the Gemini Developer API backend service var ai = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI()); // Create a `GenerativeModel` instance with a model that supports your use case var model = ai.GetGenerativeModel(modelName: "gemini-2.5-flash"); // Optionally specify existing chat history var history = new [] {  ModelContent.Text("Hello, I have 2 dogs in my house."),  new ModelContent("model", new ModelContent.TextPart("Great to meet you. What would you like to know?")), }; // Initialize the chat with optional chat history var chat = model.StartChat(history); // To generate text output, call SendMessageAsync and pass in the message var response = await chat.SendMessageAsync("How many paws are in my house?"); UnityEngine.Debug.Log(response.Text ?? "No text in response.");

Learn how to choose a model appropriate for your use case and app.

Iterate and edit images using multi-turn chat

Before trying this sample, complete the Before you begin section of this guide to set up your project and app.
In that section, you'll also click a button for your chosen Gemini API provider so that you see provider-specific content on this page.

Using multi-turn chat, you can iterate with a Gemini model on the images that it generates or that you supply.

Make sure to create a GenerativeModel instance, include responseModalities: ["TEXT", "IMAGE"] in your model configuration, and call startChat() and sendMessage() to send new user messages.

Swift 

 import FirebaseAI // Initialize the Gemini Developer API backend service // Create a `GenerativeModel` instance with a Gemini model that supports image output let generativeModel = FirebaseAI.firebaseAI(backend: .googleAI()).generativeModel(  modelName: "gemini-2.5-flash-image",  // Configure the model to respond with text and images (required)  generationConfig: GenerationConfig(responseModalities: [.text, .image]) ) // Initialize the chat let chat = model.startChat() guard let image = UIImage(named: "scones") else { fatalError("Image file not found.") } // Provide an initial text prompt instructing the model to edit the image let prompt = "Edit this image to make it look like a cartoon" // To generate an initial response, send a user message with the image and text prompt let response = try await chat.sendMessage(image, prompt) // Inspect the generated image guard let inlineDataPart = response.inlineDataParts.first else {  fatalError("No image data in response.") } guard let uiImage = UIImage(data: inlineDataPart.data) else {  fatalError("Failed to convert data to UIImage.") } // Follow up requests do not need to specify the image again let followUpResponse = try await chat.sendMessage("But make it old-school line drawing style") // Inspect the edited image after the follow up request guard let followUpInlineDataPart = followUpResponse.inlineDataParts.first else {  fatalError("No image data in response.") } guard let followUpUIImage = UIImage(data: followUpInlineDataPart.data) else {  fatalError("Failed to convert data to UIImage.") }

Kotlin 

 // Initialize the Gemini Developer API backend service // Create a `GenerativeModel` instance with a Gemini model that supports image output val model = Firebase.ai(backend = GenerativeBackend.googleAI()).generativeModel(  modelName = "gemini-2.5-flash-image",  // Configure the model to respond with text and images (required)  generationConfig = generationConfig { responseModalities = listOf(ResponseModality.TEXT, ResponseModality.IMAGE) } ) // Provide an image for the model to edit val bitmap = BitmapFactory.decodeResource(context.resources, R.drawable.scones) // Create the initial prompt instructing the model to edit the image val prompt = content {  image(bitmap)  text("Edit this image to make it look like a cartoon") } // Initialize the chat val chat = model.startChat() // To generate an initial response, send a user message with the image and text prompt var response = chat.sendMessage(prompt) // Inspect the returned image var generatedImageAsBitmap = response  .candidates.first().content.parts.filterIsInstance<ImagePart>().firstOrNull()?.image // Follow up requests do not need to specify the image again response = chat.sendMessage("But make it old-school line drawing style") generatedImageAsBitmap = response  .candidates.first().content.parts.filterIsInstance<ImagePart>().firstOrNull()?.image

Java 

 // Initialize the Gemini Developer API backend service // Create a `GenerativeModel` instance with a Gemini model that supports image output GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI()).generativeModel(  "gemini-2.5-flash-image",  // Configure the model to respond with text and images (required)  new GenerationConfig.Builder()  .setResponseModalities(Arrays.asList(ResponseModality.TEXT, ResponseModality.IMAGE))  .build() ); GenerativeModelFutures model = GenerativeModelFutures.from(ai); // Provide an image for the model to edit Bitmap bitmap = BitmapFactory.decodeResource(resources, R.drawable.scones); // Initialize the chat ChatFutures chat = model.startChat(); // Create the initial prompt instructing the model to edit the image Content prompt = new Content.Builder()  .setRole("user")  .addImage(bitmap)  .addText("Edit this image to make it look like a cartoon")  .build(); // To generate an initial response, send a user message with the image and text prompt ListenableFuture<GenerateContentResponse> response = chat.sendMessage(prompt); // Extract the image from the initial response ListenableFuture<@Nullable Bitmap> initialRequest = Futures.transform(response, result -> {  for (Part part : result.getCandidates().get(0).getContent().getParts()) {  if (part instanceof ImagePart) {  ImagePart imagePart = (ImagePart) part;  return imagePart.getImage();  }  }  return null; }, executor); // Follow up requests do not need to specify the image again ListenableFuture<GenerateContentResponse> modelResponseFuture = Futures.transformAsync(  initialRequest,  generatedImage -> {  Content followUpPrompt = new Content.Builder()  .addText("But make it old-school line drawing style")  .build();  return chat.sendMessage(followUpPrompt);  },  executor); // Add a final callback to check the reworked image Futures.addCallback(modelResponseFuture, new FutureCallback<GenerateContentResponse>() {  @Override  public void onSuccess(GenerateContentResponse result) {  for (Part part : result.getCandidates().get(0).getContent().getParts()) {  if (part instanceof ImagePart) {  ImagePart imagePart = (ImagePart) part;  Bitmap generatedImageAsBitmap = imagePart.getImage();  break;  }  }  }  @Override  public void onFailure(Throwable t) {  t.printStackTrace();  } }, executor);

Web 

 import { initializeApp } from "firebase/app"; import { getAI, getGenerativeModel, GoogleAIBackend, ResponseModality } from "firebase/ai"; // TODO(developer) Replace the following with your app's Firebase configuration // See: https://firebase.google.com/docs/web/learn-more#config-object const firebaseConfig = {  // ... }; // Initialize FirebaseApp const firebaseApp = initializeApp(firebaseConfig); // Initialize the Gemini Developer API backend service const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() }); // Create a `GenerativeModel` instance with a model that supports your use case const model = getGenerativeModel(ai, {  model: "gemini-2.5-flash-image",  // Configure the model to respond with text and images (required)  generationConfig: {  responseModalities: [ResponseModality.TEXT, ResponseModality.IMAGE],  }, }); // Prepare an image for the model to edit async function fileToGenerativePart(file) {  const base64EncodedDataPromise = new Promise((resolve) => {  const reader = new FileReader();  reader.onloadend = () => resolve(reader.result.split(',')[1]);  reader.readAsDataURL(file);  });  return {  inlineData: { data: await base64EncodedDataPromise, mimeType: file.type },  }; } const fileInputEl = document.querySelector("input[type=file]"); const imagePart = await fileToGenerativePart(fileInputEl.files[0]); // Provide an initial text prompt instructing the model to edit the image const prompt = "Edit this image to make it look like a cartoon"; // Initialize the chat const chat = model.startChat(); // To generate an initial response, send a user message with the image and text prompt const result = await chat.sendMessage([prompt, imagePart]); // Request and inspect the generated image try {  const inlineDataParts = result.response.inlineDataParts();  if (inlineDataParts?.[0]) {  // Inspect the generated image  const image = inlineDataParts[0].inlineData;  console.log(image.mimeType, image.data);  } } catch (err) {  console.error('Prompt or candidate was blocked:', err); } // Follow up requests do not need to specify the image again const followUpResult = await chat.sendMessage("But make it old-school line drawing style"); // Request and inspect the returned image try {  const followUpInlineDataParts = followUpResult.response.inlineDataParts();  if (followUpInlineDataParts?.[0]) {  // Inspect the generated image  const followUpImage = followUpInlineDataParts[0].inlineData;  console.log(followUpImage.mimeType, followUpImage.data);  } } catch (err) {  console.error('Prompt or candidate was blocked:', err); }

Dart 

 import 'package:firebase_ai/firebase_ai.dart'; import 'package:firebase_core/firebase_core.dart'; import 'firebase_options.dart'; await Firebase.initializeApp(  options: DefaultFirebaseOptions.currentPlatform, ); // Initialize the Gemini Developer API backend service // Create a `GenerativeModel` instance with a Gemini model that supports image output final model = FirebaseAI.googleAI().generativeModel(  model: 'gemini-2.5-flash-image',  // Configure the model to respond with text and images (required)  generationConfig: GenerationConfig(responseModalities: [ResponseModalities.text, ResponseModalities.image]), ); // Prepare an image for the model to edit final image = await File('scones.jpg').readAsBytes(); final imagePart = InlineDataPart('image/jpeg', image); // Provide an initial text prompt instructing the model to edit the image final prompt = TextPart("Edit this image to make it look like a cartoon"); // Initialize the chat final chat = model.startChat(); // To generate an initial response, send a user message with the image and text prompt final response = await chat.sendMessage([  Content.multi([prompt,imagePart]) ]); // Inspect the returned image if (response.inlineDataParts.isNotEmpty) {  final imageBytes = response.inlineDataParts[0].bytes;  // Process the image } else {  // Handle the case where no images were generated  print('Error: No images were generated.'); } // Follow up requests do not need to specify the image again final followUpResponse = await chat.sendMessage([  Content.text("But make it old-school line drawing style") ]); // Inspect the returned image if (followUpResponse.inlineDataParts.isNotEmpty) {  final followUpImageBytes = response.inlineDataParts[0].bytes;  // Process the image } else {  // Handle the case where no images were generated  print('Error: No images were generated.'); }

Unity 

 using Firebase; using Firebase.AI; // Initialize the Gemini Developer API backend service // Create a `GenerativeModel` instance with a Gemini model that supports image output var model = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI()).GetGenerativeModel(  modelName: "gemini-2.5-flash-image",  // Configure the model to respond with text and images (required)  generationConfig: new GenerationConfig(  responseModalities: new[] { ResponseModality.Text, ResponseModality.Image }) ); // Prepare an image for the model to edit var imageFile = System.IO.File.ReadAllBytes(System.IO.Path.Combine(  UnityEngine.Application.streamingAssetsPath, "scones.jpg")); var image = ModelContent.InlineData("image/jpeg", imageFile); // Provide an initial text prompt instructing the model to edit the image var prompt = ModelContent.Text("Edit this image to make it look like a cartoon."); // Initialize the chat var chat = model.StartChat(); // To generate an initial response, send a user message with the image and text prompt var response = await chat.SendMessageAsync(new [] { prompt, image }); // Inspect the returned image var imageParts = response.Candidates.First().Content.Parts  .OfType<ModelContent.InlineDataPart>()  .Where(part => part.MimeType == "image/png"); // Load the image into a Unity Texture2D object UnityEngine.Texture2D texture2D = new(2, 2); if (texture2D.LoadImage(imageParts.First().Data.ToArray())) {  // Do something with the image } // Follow up requests do not need to specify the image again var followUpResponse = await chat.SendMessageAsync("But make it old-school line drawing style"); // Inspect the returned image var followUpImageParts = followUpResponse.Candidates.First().Content.Parts  .OfType<ModelContent.InlineDataPart>()  .Where(part => part.MimeType == "image/png"); // Load the image into a Unity Texture2D object UnityEngine.Texture2D followUpTexture2D = new(2, 2); if (followUpTexture2D.LoadImage(followUpImageParts.First().Data.ToArray())) {  // Do something with the image }

Stream the response

Before trying this sample, complete the Before you begin section of this guide to set up your project and app.
In that section, you'll also click a button for your chosen Gemini API provider so that you see provider-specific content on this page.

You can achieve faster interactions by not waiting for the entire result from the model generation, and instead use streaming to handle partial results. To stream the response, call sendMessageStream().


What else can you do?

Try out other capabilities

Learn how to control content generation

You can also experiment with prompts and model configurations and even get a generated code snippet using Google AI Studio.

Learn more about the supported models

Learn about the models available for various use cases and their quotas and pricing.


Give feedback about your experience with Firebase AI Logic