Posted by Amman Asfaw, Product Manager, Android Studio Android Studio Quail 2 is now stable and ready for you to use in production, bringing a shift to your IDE with concurrent agentic workflows, natively integrated memory leak profiling, and context-aware crash remediation. Whether you are performing a sweeping architectural overhaul, tracing a memory leak, or resolving a critical production crash, Android Studio keeps you anchored in your workspace by reducing manual friction. Here’s a deep dive into what’s new: Multi-tasking with parallel chats In Android Studio Quail 2, we've been hard at work redesigning Agent Mode from the ground up. This new architecture provides better performance, offers more flexibility for decomposing complex tasks, and improves the suite of internal tools the agent uses to do its work. In addition to these behind-the-scenes improvements, these changes also allow you to converse across multiple agent chats simultaneously. Waiting for the Android Studio agent to finish a task before you can ask another question or initiate a separate task in Agent Mode is a bottleneck of the past. You can multi-task seamlessly: kick off a UI refactor in one tab, fix a ProGuard rule in a second, and generate documentation in a third. You can also change which models the agent uses from chat to chat based on the requests you have. Take a look at Android Bench for an analysis of how LLMs perform Android development tasks. How to use: Click the "+" icon to start a new parallel conversation, and use the History icon to navigate between active tasks. Alternatively, select File > New > New Agent Tab to open a conversation in a dedicated tab. Note: Worktree support is currently unavailable. Exercise caution when running concurrent chats that modify the same project files, which can potentially lead to editor conflicts. Run multiple agent tasks in parallel with different models of your choice. Use the History icon to navigate between active tasks. Memory leak detection with LeakCanary Memory leaks in Android occur when your code holds onto an object's reference long after its life cycle has ended. This prevents the Garbage Collector from reclaiming that memory, eventually leading to sluggish performance or OutOfMemoryError . Hunting down memory leaks can be a tedious, manual task. Starting with Android Studio Quail 2, the popular open-source leak detector LeakCanary is natively integrated directly into the Profiler as a dedicated, first-class task. This integration transforms your debugging performance by lifting and shifting the heap analysis off your resource-constrained testing phone, and onto your powerful development computer. By running the analysis on your computer, leak tracing is up to five times faster and jank-free, leaving your test app running smoothly on the device. Once a leak is detected during a profiling session: The Profiler renders an interactive, color-coded leak trace, grouping occurrences and estimating lost memory. You can click Go to declaration on any leaking object in the trace to instantly jump to that exact line of code in your editor. You can click Fix with Agent to have the Gemini agent ingest the trace, explain the root cause of the retained reference, and write the exact code change (such as unbinding a listener or clearing a static reference) to plug the leak. Review memory leaks identified via LeakCanary through the Fix with Agent button. App Quality Insights agent integration Tracking down the root cause of an app crash can require manually synthesizing stack traces, device data, and source code. However Android Studio’s App Quality Insights (AQI) is now fully integrated with Agent Mode to do the heavy lifting for you. When you click on a crash in the AQI panel, you immediately get a concise, high-level summary of the issue. If you need to dig deeper, simply click See more . This opens a dedicated chat where the agent uses your selected model and pulls in local source code and the full stack trace to deliver a comprehensive explanation of the failure. With the new agent integration, you move directly from issue identification to resolution. By clicking Fix with AI , the agent will analyze the issue, propose a step-by-step fix plan, and—upon your approval—apply the necessary code changes directly to your project and verify the resulting fix The Fix with AI button triggering the agent to analyze the issue, then propose the fix Quality & stability improvements Beyond new features, we’ve continued our focus on quality by addressing numerous bugs and incorporating the latest stability and performance improvements from the IntelliJ platform, making this a significant enhancement for your daily development. Get Started Ready to dive in and accelerate your development? Download Android Studio Quail 2 and start exploring these new features today! As always, your feedback is crucial to us. Check known issues , report bugs , and be part of our vibrant community on LinkedIn , Medium , YouTube , or X .
Posted by Zoe Lopez-Latorre, Senior Developer Relations Engineer, Android Back in March, we introduced Android Bench —our LLM leaderboard for real-world Android development tasks. Our goal was to provide transparency around model capabilities in Android development and to encourage model improvements, to give you more helpful AI options for your everyday workflow. Since then, we have enhanced the benchmark based on your feedback, including evaluating open-weight models and adding cost and efficiency dimensions to the leaderboard. But AI capabilities are ever-evolving, and measurement needs to follow suit. As part of our July release, we have adopted the Harbor framework , which includes an updated version of the benchmarking agent used to evaluate models. Along with this change to our evaluation, in this July release we’re adding 8 new models ( Claude Fable 5, Claude Sonnet 5, Claude Opus 4.8, GLM 5.2, Kimi K2.7 Code, MiniMax M3, Qwen 3.7 Plus and Qwen 3.7 Max ) to the leaderboard. We’re also sharing opportunities for you, the Android developer community, to contribute to the benchmark. Upgrading our methodology with the Harbor framework When we designed Android Bench, we anchored our methodology on leading industry standards available at the time. We used mini-swe-agent v1, a general-purpose benchmarking agent, and adapted it to the nuances of Android development to provide a baseline measurement for the capabilities of models for common Android development tasks. To continue providing you with state-of-the-art evaluations that accurately measure the latest model capabilities on Android development, we are standardizing our benchmark to the Harbor framework . Harbor defines standards and integrations that make it easy for anyone to run the benchmark, evaluate their preferred set-up, or share results – providing you with additional transparency and visibility. This upgrade enables us to more rigorously evaluate models and their capabilities, and we re-ran the benchmark on all models to establish an updated baseline. This means there is a minor shift in scoring, but you will still be able to view historical scores within the archive on our website. We want to ensure Android Bench is helpful for you, so we will continuously update it as our evaluations and the industry mature. Expanding the leaderboard with 8 new models As part of our commitment to keeping the leaderboard fresh, we have added Claude Fable 5, Claude Sonnet 5, Claude Opus 4.8, GLM 5.2, Kimi K2.7 Code, MiniMax M3, Qwen 3.7 Plus and Qwen 3.7 Max to the Android Bench leaderboard. You will see that Claude Fable 5 is at the top of the leaderboard with a score of 84.5, followed by GPT 5.5 with 80.2, with Claude Sonnet 5 in 3rd with a score of 76.2. When just comparing Open-weight models, GLM 5.2 is at the top with 72.2, followed by Kimi K2.7 Code with a score of 70.4. You can check out model performance and efficiency metrics on the updated leaderboard to see how these new and previous models navigate Android-specific challenges like Jetpack Compose migrations, wearable networking, and platform API updates. Opening Android Bench to community contributions From the beginning, we’ve valued an open and transparent approach, which is why we made our original methodology and test harness publicly available on GitHub. You’ve asked for a way to provide feedback on our dataset, so now we’re taking collaboration a step further by giving you, the Android developer community, a chance to shape Android Bench. Starting today, you can contribute to Android Bench in two ways: Design and submit your own Android development tasks to evaluate how models handle the scenarios that matter to you. Run and share benchmark evaluations firsthand, testing your preferred models against our dataset or your own custom tasks. We will be reviewing the submitted tasks and will be assessing if they get added to the benchmark. We hope to build a benchmark that truly reflects the diverse, day-to-day realities of the global Android developer community. Looking ahead With more and more options for agentic development, maintaining a cutting-edge benchmark ensures that the AI assistance you rely on keeps getting smarter, more helpful, and more effective. Head over to our GitHub repository to check out the tasks. We invite you to submit a task to our team for review, and you can check out Harbor Hub to explore the dataset or submit evaluations. As always, you can find the updated leaderboard , or read the methodology on our website. Android Bench, LLM leaderboard, Harbor framework, Android development, Claude Fable 5, GPT 5.5, Claude Sonnet 5, GLM 5.2, Kimi K2.7 Code, MiniMax M3, Qwen 3.7 Plus, Qwen 3.7 Max, AI benchmarking, Jetpack Compose migration, wearable networking, mobile AI agent, Zoe Lopez-Latorre, model evaluation, open-weight models, developer community contributions.
Posted by Matthew Forsythe, Director Product Management, Android App Safety July 15, 2026: Updated Play Console requirements for Play developers To meet Android developer verification and updated Play Console Requirements , Play developers must register their Play apps in Play Console. While 99% of apps on Play have been registered automatically, you should check your Play Console Home page to register any remaining apps by September 30, 2026 to avoid global removal from Google Play and ensure a seamless user installation experience. You can also use Play Console to register apps you distribute outside of Google Play to ensure they can be installed on certified Android devices. Last year, we introduced Android developer verification to strengthen ecosystem security and stop malicious actors from hiding behind anonymity to release harmful apps. Millions of apps have been registered since the verification launched in March, covering nearly all installs on Google Play and a large majority of installs from outside of Google Play. We appreciate the feedback and partnership from industry leaders, developers, and Android communities that helped us design this experience and drive strong adoption. Initial launch across seven stores and four countries These new developer verification protections will take effect on September 30, 2026, starting with users in Brazil, Indonesia, Singapore, and Thailand. This rollout is an industry-wide effort to create a safer ecosystem . We will begin by verifying app installations from the following stores: Google (Google Play) Honor (HONOR App Market) OPlus (OPPO App Market) Samsung (Galaxy Store) Transsion (Palm Store) vivo (V-Appstore) Xiaomi (GetApps) Following this initial phase with our partners, we will expand these protections globally for all apps on certified Android devices in 2027. Automate your workflow with new APIs To further streamline app registration, we are launching a suite of developer-requested APIs to help you register apps in bulk or directly through your continuous integration and deployment (CI/CD) pipelines. The Android Developer ID Status API will let you check if a package name has already been registered, and the Android Developer Console API will let you register and manage package names directly within your development environment. Both APIs also support OAuth delegation, allowing third-party platforms, like Android app stores, to perform these operations natively on your behalf. We'll launch these APIs over the next few months. What’s next June 2026: Starting this month, we are rolling out a new system service that will be automatically installed on most Android devices. This service will be used later this year to verify developer registration. July 2026: We’ll launch the Android Developer ID Status API globally and begin early access for the Android Developer Console API. Early access also starts for limited distribution accounts on Android Developer Console. This new type of Android developer account is designed for students, hobbyists, and learners and lets you share your apps to up to 20 devices without a government-issued ID or a fee. August 2026: Limited distribution accounts and the new Android Developer Console API will launch globally. We’ll also launch an advanced flow for installing apps from unverified developers, which includes security checkpoints to resist coercion scams, while allowing power users to maintain the ability to sideload apps from unverified developers. September 30, 2026: App registration becomes required for participating stores in Brazil, Indonesia, Singapore, and Thailand . Unregistered apps can be sideloaded with Android Debug Bridge (adb) or advanced flow. 2027 and beyond: After incorporating the feedback from our partners, users, and developer community, we’ll expand the Android verification requirement globally. Get started with Android developer verification If you distribute apps in Brazil, Indonesia, Singapore, or Thailand via the stores listed above, please ensure your verification is complete by the September deadline. Google Play developers: Most Play developers are already verified, and over 99% of their apps have been registered. Go to your Play Console Home page to see your app’s verification status, and register apps you want to continue distributing that weren't automatically registered. Developers who distribute only outside of Google Play: Sign up for the Android Developer Console today to register your apps. Students and hobbyists: Sign up here for early access to limited distribution accounts to help us refine the feature with your feedback. Thank you for helping us build a safer Android ecosystem. Stay tuned for more updates as we approach September and the 2027 global rollout.
Posted by Coco Fatus, UX Designer, Alon Hetzroni, UX Engineer, Azin Mehrnoosh, Product Manager Android XR At this year's Google I/O , we announced an update for spatial experiences: the Geospatial API is now available as a preview in ARCore for Jetpack XR . By bringing Google's Visual Positioning System (VPS) to Android XR, Android XR enables anchoring digital content to the physical world with sub-meter accuracy and precise orientation in supported areas.* To explore what the Geospatial API could unlock, our team built a demo: the XR Geospatial Tour. Imagine walking into a new city, putting on a pair of wired XR glasses (like the upcoming XREAL Project Aura), and instantly having a knowledgeable, local guide showing you around. You don't need to stare down at a 2D map—instead, 3D models gently guide your path, and an intelligent voice tells you about the historical landmarks right in front of you. We combined the Geospatial APIs , Gemini API using Firebase AI Logic , Google Maps Grounding , and Jetpack XR SDK to create a hands-free, immersive walking tour experience. *Disclaimer: Video and Tour Guide application are for demonstration purposes only. Some sequences have been shortened. Any hardware depicted may be under development; final product details may differ. Let’s walk through the implementation details and show how we tied these APIs together to build a world-scale spatial experience. 1. Pinpointing the User with ARCore Geospatial API (VPS) Enhance your navigation experience on XR by combining the power of GPS with the precision of VPS. The accuracy and precise orientation that comes with VPS allows 3D waypoints to align with the physical world. This is why the Geospatial API on Android XR can help you build custom experiences. By using advanced computer vision, VPS tries to provide a GeospatialPose (including latitude, longitude, and heading) that is more accurate than GPS. Here's how we retrieve the user's Geospatial pose by mapping the device's orientation to a Geospatial coordinate: // Retrieve the current geospatial pose from the ARCore session val result = geospatial.createGeospatialPoseFromPose(arDevice.state.value.devicePose) if (result is CreateGeospatialPoseFromPoseSuccess) { val pose = result.pose Log.d("VPS", "Accurate Location: ${pose.latitude}, ${pose.longitude}") } Because the entire experience relies on this accuracy, we monitor the horizontalAccuracy and orientationYawAccuracy until they meet our thresholds. If the user is indoors or in an unrecognized area, we prompt them to "walk to an outdoor public space and look around". 2. Crafting the Itinerary with Gemini API & Google Maps Grounding Once we have a location, we use the Gemini API using Firebase AI Logic to prompt the Gemini model to act as a local tour guide. We pass the user's coordinates to the model and ask it to output a structured JSON response containing nearby walking tours: val configForTools = ToolConfig( functionCallingConfig = null, retrievalConfig = retrievalConfig { latLng = FirebaseLatLng(pose.latitude, pose.longitude) languageCode = "en" } ) val responseJsonSchema = Schema.obj( mapOf( "locationIntro" to Schema.string(), "tours" to Schema.array( Schema.obj( mapOf( "title" to Schema.string(), "description" to Schema.string(), "stops" to Schema.array( Schema.obj( mapOf( "name" to Schema.string(), "detailedName" to Schema.string(), "description" to Schema.string() ) ) ) ) ) ) ) ) val model = Firebase.ai(backend = GenerativeBackend.googleAI()).generativeModel( modelName = "gemini-3.5-flash", tools = listOf(Tool.googleMaps()), generationConfig = generationConfig { responseMimeType = "application/json" responseSchema = responseJsonSchema } ) val result = model.generateContent("The user is at latitude ${pose.latitude} and longitude ${pose.longitude}. Generate exactly 3 diverse tours near this location (e.g., historical, food, nature). All tour ideas should be walking distance only.") Large Language Models are great at generating rich descriptions, but they can sometimes hallucinate exact latitude/longitude coordinates. To solve this, we used Google Maps Grounding to ground the AI. 3. A Voice to Guide You: Gemini 2.5 TTS To make the tour guide feel truly present, we implemented dynamic voiceovers. Using the gemini-2.5-flash-tts model, we can configure our model generation config to natively return audio data instead of just text! Here’s how you can request the ResponseModality.AUDIO: val ttsModel = Firebase.ai(backend = GenerativeBackend.googleAI()) .generativeModel( modelName = "gemini-2.5-flash-tts", generationConfig = generationConfig { // Instruct the model to return Audio responseModalities = listOf(ResponseModality.AUDIO) } ) val response = ttsModel.generateContent("Say in a neutral but positive voice:\n$prompt") // Extract the raw audio bytes from the response val audioBytes = response.candidates.firstOrNull()?.content?.parts ?.filterIsInstance<InlineDataPart>() ?.firstOrNull { it.mimeType.contains("audio") }?.inlineData 4. Bringing it to Life in 3D with Jetpack XR The final piece of the puzzle is rendering this data in the user's field of view. The Jetpack XR SDK makes it intuitive to transition from a 2D Android UI to spatial computing. We used Jetpack Compose for XR to build spatial components. To represent points of interest along the tour, we built a Composable called InfoSphere, which contains a GltfModel of a 3D orb that floats in space and can be interacted with to reveal information. Using Jetpack XR SDK, we can place 3D models alongside the Compose UI using SpatialBox and SceneCoreEntity . We also used InteractableComponent to respond to user taps. @Composable fun InfoSphere( content: InfoBubbleContent, session: Session, sphereModel: GltfModel, isSelected: Boolean, onClick: () -> Unit ) { // SpatialBox lets us arrange 3D components and SpatialPanels together SpatialBox( SubspaceModifier .offset(x = 2.dp, y = 1.dp, z = (-3).dp) // Positioned in 3D space ) { // Smoothly animate the visibility of our 2D Compose UI Panel AnimatedSpatialVisibility(visible = isSelected) { SpatialPanel { InfoBubble(content) // Regular 2D Compose UI } } // Render our interactive 3D sphere SceneCoreEntity( factory = { GltfModelEntity.create(session, sphereModel).also { entity -> // Make the 3D model respond to user taps entity.addComponent(InteractableComponent.create(session) { inputEvent -> if (inputEvent.action == InputEvent.Action.UP) { onClick() } }) } } ) } } By combining AnimatedSpatialVisibility for traditional Compose UI surfaces with SceneCoreEntity 3D elements, we're able to seamlessly blend data into the physical world. Explore what’s possible with Android XR today Building the XR Geospatial Tour app showed us that the barrier to entry for world-scale spatial experiences is lower than ever for Android developers. With the Geospatial API now available in preview on Android XR, your apps can seamlessly understand the physical world around them. By combining Compose for XR ’s APIs with the high-precision location data of VPS and the generative capabilities of Gemini, we can create experiences that understand both where the user is and what they are looking at. To help you get hands-on with Android XR, we are thrilled to open applications for the Android XR Developer Catalyst Program , which includes XREAL Project Aura. Starting today, you can apply to get access to an XREAL Project Aura devkit or our display glasses devkit over the coming months! *Disclaimer: Available on select devices. Internet connection required. Works on compatible apps and surfaces. Results may vary.
