Google’s Agent Development Kit (ADK) for Android lets you build and integrate AI agents in Android apps. The Android library supports agent workflows using hosted models or on-device inference, so you can start with a single tool-using agent and expand toward multi-agent workflows as your app needs them.
What Android ADK is—and what it is not
Android ADK is Google’s Android-oriented library for adding AI agents to Android applications. The broader ADK framework supports agents that can use tools and coordinate workflows; the Android guide adapts that approach to an Android app’s build configuration and runtime.
It is not simply a model API. A model produces responses, while an agent can also be given instructions and tools that let it take actions or retrieve app-specific information. Your app remains responsible for the user experience, permissions, data handling, and deciding which actions an agent may perform.
Check the Android project requirements
The Android Developers guide accessed on October 4, 2026 lists Android Studio, compileSdk 34 or higher, and minSdk 24 or higher as prerequisites. SDK levels are technical requirements, not performance results; check the current guide before adopting them because Android tooling and library compatibility can change.
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The guide’s Kotlin Gradle example uses the Android, Kotlin, and KSP plugins, a Java 17 toolchain, and these example dependencies:
implementation("com.google.adk:google-adk-kotlin-core-android:0.1.0")
ksp("com.google.adk:google-adk-kotlin-processor:0.1.0")Here, 0.1.0 is the version shown in that documentation example, not a claim that it is the latest release. Use the versions and plugin configuration in the current Android guide when setting up a real project.
Use the Android core in an Android app
The Android artifact, google-adk-kotlin-core-android, replaces the JVM core dependency for an Android project. Do not include both core artifacts in the same Android configuration. The Kotlin processor is configured through KSP in the documented setup.
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Build up from one agent and one tool
A useful first milestone is an agent with a clear instruction and one narrowly scoped tool. Keep the tool’s responsibility explicit: for example, a tool might look up a saved item in your app’s own data layer. The behavior below is illustrative only; it is not a tested integration or a complete, version-pinned program.
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// Illustrative Kotlin shape; adapt imports and API details to the current ADK guide.
class SavedItemTools {
@Tool
fun findSavedItem(
@Param("The name or phrase to search for") query: String
): String {
// Replace this illustrative response with an authorized app data lookup.
return "No saved-item lookup is connected yet."
}
}In an actual app, the tool should call a defined application service, validate inputs, and return only information the current user is allowed to access. Keep side effects—such as sending a message or changing account settings—behind deliberate app-level checks rather than assuming an agent’s decision is sufficient authorization.
Android agent code follows the ADK Kotlin patterns, including annotated tools and parameters. The Android-specific differences are primarily project setup and runtime invocation; the Android guide points to the Kotlin quickstart for the broader agent API patterns.
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Choose where inference runs
The Android guide describes hosted model execution as well as a local path using Gemini Nano through ML Kit GenAI APIs. In that path, the guide creates an ML Kit GenerativeModel, wraps it with GenaiPrompt.create, and supplies the resulting adapter as the agent’s model. Use the current Android documentation for exact initialization and invocation code, since API support and setup can evolve.
| Approach | What it can suit | Trade-off to consider |
|---|---|---|
| Hosted model | Agent tasks that rely on a hosted service or a cloud-oriented workflow. | Requires the network and service configuration relevant to that model. |
| On-device Gemini Nano via ML Kit | Selected tasks intended to run locally, including workflows designed to operate without network access. | Device compatibility, model availability, and performance depend on the supported environment; the cited guide does not provide an independent compatibility or performance study. |
| Hybrid cloud and device | A workflow where cloud orchestration is combined with on-device subagents for selected privacy-sensitive tasks. | Requires deliberate routing: decide which inputs and work stay local and which are sent to hosted services. |
On-device inference can support offline operation and keep selected processing on the device, but those capabilities do not by themselves establish a privacy guarantee. Review what data each tool accesses, what any hosted component receives, and the applicable platform and service behavior for the devices you support.
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Grow the workflow only when the app needs it
Google’s ADK learning materials describe a progression beyond a single agent. Add complexity to solve a real workflow problem, not merely to make the architecture look more advanced.
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Multiple tools
Give one agent several tools when it must choose among distinct capabilities, such as searching app content and checking a user’s saved preferences. Keep tool descriptions and boundaries specific so the agent can select appropriately and your app can enforce access rules.
Agent teams and delegation
For work that naturally separates into specialist tasks, ADK tutorials cover agent teams, delegation, session management, and safety callbacks. This structure can make responsibilities clearer, but it also adds coordination and state-management decisions. Define which agent owns each task and what information may pass between them.
Streaming
Streaming is useful when the interface should display output incrementally rather than waiting for a complete response. Treat partial output as incomplete: do not trigger consequential actions solely because an early streamed segment appears to request them.
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Develop, evaluate, and decide on deployment
Start by exercising the agent and tools in the Android app’s development environment. Test expected requests as well as malformed input, unavailable network conditions where relevant, and tool failures. Check that the app handles an agent response that is incomplete, unhelpful, or unsuitable for display.
The wider ADK documentation also discusses evaluation and deployment choices, including Cloud Run and Google Kubernetes Engine. Those are broader framework options rather than requirements for building an Android client. Select a deployment target only if your architecture includes a service that needs to run there.
Quick Recap
- Keep model credentials and privileged service secrets out of the Android client.
- Limit tools to the data and actions required for the task.
- Handle timeouts, service errors, and unavailable device capabilities in the app UI.
- For a hybrid design, make the local-versus-hosted routing decision explicit and test the behavior when connectivity changes.
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