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Android Studio Panda 3 is now stable and ready for you to use in production. This release gives you even more control and customization over your AI-powered workflows, making it easier than ever to build high-quality Android apps.
Whether you're bringing new capabilities to an existing app or standing up a brand new app, these updates elevate your development experience by allowing your AI Agent in Android Studio to learn your specific practices and giving you granular control over its permissions.
Lastly, in addition to AI skills and Agent Mode enchantments, Android Studio Panda 3 also includes updated support for build Android apps for cars.
Here’s a deep dive into what’s new:
Create a more helpful AI agent by using agent skills in Android Studio. Agent skills are specialized instructions that teach the agent new capabilities and best practices for a specific workflow, which the agent can then leverage as needed. This significantly reduces the level of detail required for your day-to-day prompts. Agent skills work with Gemini in Android Studio or with other remote 3rd party LLMs you integrate into the agent framework in Android Studio.
You and members of your team can create skills that tell the agent exactly how you want to handle specific tasks in your codebase. For example, you could create a custom “code review” skill tailored to your organization's coding standards, or custom skill to provide the agent with more information on using an in-house library.
Once you have created a skill, the agent will be able to use it automatically, or you can manually trigger it by typing @ followed by the skill name. Check out the documentation to learn more about how to create skills for your codebase, or better yet—ask your agent to help you build a new skill and it will guide you through the details!
Manually Trigger Agent Skill in Android StudioTo build a skill for your project, do the following:
Agent skills in Android StudioYou control your codebase, and you can now be more deliberate with which data and capabilities you choose to share with AI agents. The new granular agent permissions in Android Studio let you decide exactly what agents can do for you.
When Agent Mode needs to read files, run shell commands, or access the web, it explicitly asks for your permission. We know that 'approval fatigue' is a real risk in AI workflows—when a tool asks for permission too often, it’s easy to start clicking 'Allow' without fully reviewing the action. By offering granular 'Always Allow' rules for trusted operations and an optional sandbox for experimental ones, Android Studio helps you stay focused on the high-stakes decisions that actually require your manual sign-off.
Agent permissions are intuitive to set up and use. For example, granting high-level permissions automatically authorizes related sub-tools, while commands you have previously approved will run automatically without interrupting your flow. Rest assured, accessing sensitive files like SSH keys will always require your explicit sign-off.
For even more security, you can also use an optional sandbox to enforce strict, isolated control over the agent.
We’re making it easier to build Android apps for cars. Building apps for the car used to mean wrestling with complex configurations just to get the project to build successfully.
Now, you can accelerate your development with the new “Empty Car App Library App” template in Android Studio. This template takes care of the required boilerplate code for a driving-optimized app on both Android Auto and Android Automotive OS, saving you significant time and effort. Instead of getting bogged down in setup, you can focus on creating the best experience for your users on the road.
To use the new template:
Panda 3 builds off last month’s AI-focused Panda 2 release. Check out Go from prompt to working prototype with Android Studio Panda 2 post to learn more about new Android Studio features, including the AI-powered New Project Flow that takes you from prompt to prototype and the Version Upgrade Assistant that takes the toil out of updating your dependencies.
Dive in and accelerate your development. Download Android Studio Panda 3 and start exploring these powerful new agentic 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. Happy coding!
At Google, we’re committed to bringing the most capable AI models directly to the Android devices in your pocket. Today, we’re thrilled to announce the release of our latest state-of-the-art open model: Gemma 4.
These models are the foundation for the next generation of Gemini Nano, so code you write today for Gemma 4 will automatically work on Gemini Nano 4-enabled devices that will be available later this year. With Gemini Nano 4, you’ll benefit from our additional performance optimizations so you can ship to production across the Android ecosystem with the most efficient on-device inference.
You can get early access to this model today through the AICore Developer Preview.
Because Gemma 4 natively supports over 140 languages, you can expect improved localized, multilingual experiences for your global audience. Furthermore, Gemma 4 offers industry-leading performance with multimodal understanding, allowing your apps to understand and process text, images, and audio. To give you the best balance of performance and efficiency, Gemma 4 on Android comes in two sizes:
The new model is up to 4x faster than previous versions and uses up to 60% less battery. Starting today, you can experiment with improved capabilities including:
Join the Developer Preview today to download these models in preview models and start building next-generation features right away.
You can try out the model without code by following the Developer Preview guide. If you want to jump straight into integrating these models with your existing workflow, we’ve made that seamless. Head over to Android Studio to refine your prompt and build with the familiar ML Kit Prompt API. We’ve introduced a new ability to specify a model, allowing you to target the E2B (fast) or E4B (full) variants for testing.
// Define the configuration with a specific track and preference val previewFullConfig = generationConfig { modelConfig = ModelConfig { releaseTrack = ModelReleaseTrack.PREVIEW preference = ModelPreference.FULL } } // Initialize the GenerativeModel with the configuration val previewModel = GenerativeModel.getClient(previewFullConfig) // Verify that the specific preview model is available val previewModelStatus = previewModel.checkStatus() if (previewModelStatus == FeatureStatus.AVAILABLE) { // Proceed with inference val response = previewModel.generateContent("If I get 26 paychecks per year, how much I should contribute each paycheck to reach my savings goal of $10k over the course of a year? Return only the amount.") } else { // Handle the case where the preview model is not available // (e.g., print out log statements) }
The goal of this Developer Preview is to give you a head start on refining prompt accuracy and exploring new use cases for your specific apps.
We will be making several updates throughout the preview period, including support for tool calling, structured output, system prompts, and thinking mode in Prompt API, making it easier to take full advantage of the new capabilities and significant performance optimizations in Gemma 4.
The preview models are available for testing on AICore-enabled devices. These models will run on the latest generation of specialized AI accelerators from Google, MediaTek, and Qualcomm Technologies. On other devices, the models will initially run on a CPU implementation that is not representative of final production performance. If your device is not AICore-enabled, you can also test these models via the AI Edge Gallery app. We’ll provide support for more devices in the future.
Ready to see what Gemma 4 can do for your users?
Every developer's AI workflow and needs are unique, and it's important to be able to choose how AI helps your development. In January, we introduced the ability to choose any local or remote AI model to power AI functionality in Android Studio, and today, we're announcing the availability of Gemma 4 for AI coding assistance in Android Studio. This new local model trained on Android development provides the best of both worlds: the privacy and cost-efficiency of on-device processing alongside state-of-the-art reasoning and tool-calling capabilities.
By running locally on your machine, Gemma 4 gives you AI code assistance that doesn't require an internet connection or an API key for its core operations. Key benefits include:
Gemma 4 was trained for Android development with agentic tool calling capabilities. When you select Gemma 4 as your local model, you can leverage Agent Mode for a variety of development use cases, such as:
The 26B MoE is recommended for Android app developers using a machine with the minimum hardware requirements. Total RAM needed includes both Android Studio and Gemma.
| Model | Total RAM needed | Storage needed |
|---|---|---|
| Gemma E2B | 8GB | 2 GB |
| Gemma E4B | 12 GB | 4 GB |
| Gemma 26B MoE | 24 GB | 17 GB |
For a detailed walkthrough on configuration, check out the official documentation on how to use a local model.
We are excited to see how Gemma 4 enables more private, secure, and powerful development workflows. As always, your feedback is essential as we continue to refine the AI experience in Android Studio. If you find a bug or issue, please file an issue. Also you can be part of our vibrant Android developer community on LinkedIn, YouTube, or X. Happy coding!
Today, we are enhancing Android development with Gemma 4, our latest state-of-the-art open model designed with complex reasoning and autonomous tool-calling capabilities.
Our vision is to enable local agentic AI on Android across the entire software lifecycle, from development to production. Android supports a range of Gemma 4 models, from the most efficient ones running directly on-device in your apps to more powerful ones running on your development machine to help you build apps. We are bringing Gemma 4 to Android developers through two pillars:
When building Android apps, Android Studio can use Gemma 4 to leverage its state-of-the-art reasoning power and native support for tool use, while keeping the model and inference contained entirely on your local machine.
Gemma 4 was trained on Android development and designed with Agent Mode in mind. This means that when you select Gemma 4 as your local model, you can leverage the full suite of Agent Mode capabilities for a variety of Android development use cases, including refactoring legacy code, building an entire app or new features, and applying fixes iteratively.
Learn more about the possibilities Gemma 4 brings to your app development flow and how to get started.
Since the introduction of Gemini Nano as the foundation model on Android, it has become available on over 140 million devices. Gemma 4 is the base model for the next generation of Gemini Nano (Gemini Nano 4) that is optimized for performance and quality on Android devices. This model is up to 4x faster than the previous version and uses up to 60% less battery.
To make it as easy as possible to preview and prototype with Gemma 4 E2B and E4B models directly on AICore-supported devices, we’re launching the AICore Developer Preview. While we continue to expand the ML Kit GenAI Prompt API surface to unlock additional advanced capabilities of the model, you can already start exploring new use cases with Gemma 4 using the Prompt API.
Prepare your apps for the launch of the Gemini Nano 4 on the new flagship Android devices later this year by prototyping with Gemma 4 today. Read about the upcoming features and deep dive into AICore Developer Preview and its Gemma 4 support here.
Running Gemma 4 locally, you can leverage its advanced reasoning and tool-calling capabilities in your entire workflow, from developing with the AI coding assistant in Android Studio to shipping intelligent features in your app with ML Kit GenAI Prompt API. This local-first approach, available under Gemma’s open Apache license, provides an alternative for developers to innovate in a privacy-centric and cost effective manner. We're updating Android Bench to include Gemma 4 and other open models, providing the quantified data you need to navigate performance trade-offs and select the best model for your use case.
We can’t wait to see what you build!
Google and Back Market's new USB kit makes it easy to install ChromeOS Flex, a free OS to revive your PC or Mac and reduce e-waste.
We are hosting Search Central Live Shanghai 2026 on May 15. This event is structured for the local community, with a specific focus on optimizing sites that target users outside of China.
Hello Everyone! We've just released Chrome 147 (147.0.7727.49) for Android to a small percentage of users. It'll become available on Google Play over the next few days. You can find more details about early Stable releases here.
This release includes stability and performance improvements. You can see a full list of the changes in the Git log. If you find a new issue, please let us know by filing a bug.
64-bit architectures provide performance improvements and a foundation for future innovation, delivering faster and richer experiences for your users. We’ve supported 64-bit CPUs since Android 5. This aligns Wear OS with recent updates for Google TV and other form factors, building on the 64-bit requirement first introduced for mobile in 2019.
Today, we are extending this 64-bit requirement to Wear OS. This blog provides guidance to help you prepare your apps to meet these new requirements.
Starting September 15, 2026:
We are not making changes to our policy on 32-bit support, and Google Play will continue to deliver apps to existing 32-bit devices.
The vast majority of Wear OS developers has already made this shift, with 64-bit compliant apps already available. For the remaining apps, we expect the effort to be small.
Many apps are written entirely in non-native code (i.e. Kotlin or Java) and do not need any code changes. However, it is important to note that even if you do not write native code yourself, a dependency or SDK could be introducing it into your app, so you still need to check whether your app includes native code.
The 64-bit version of your app should offer the same quality and feature set as the 32-bit version. The Wear OS Android Emulator can be used to verify that your app behaves and performs as expected in a 64-bit environment.
Note: Since Wear OS apps are required to target Wear OS 4 or higher to be submitted to Google Play, you are likely already testing on these newer, 64-bit only images.
When testing, pay attention to native code loaders such as SoLoader or older versions of OpenSSL, which may require updates to function correctly on 64-bit only hardware.
We are announcing this requirement now to give developers a six-month window to bring their apps into compliance before enforcement begins in September 2026. For more detailed guidance on the transition, please refer to our in-depth documentation on supporting 64-bit architectures.
This transition marks an exciting step for the future of Wear OS and the benefits that 64-bit compatibility will bring to the ecosystem.
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