Menu

Individual skill

litert-runtime

Configures and implements LiteRT (.tflite) on-device inference on Android in Kotlin using the CompiledModel API on CPU and GPU. Use this skill for any Android LiteRT or .tflite task, including Gradle setup, image classification or vision/audio/embedding helpers, TensorBuffer reuse, CPU memory optimization, Bitmap preprocessing, AndroidViewModel GPU-to-CPU fallback with warm-up on a single-thread dispatcher, LiteRT in Google Play services (play-services-litert), migrating from legacy org.tensorflow.lite.Interpreter, GpuDelegate, or play-services-tflite, and latency benchmarking. Don't use for running generative text LLMs with .litertlm files or for Python model conversion.

device-ai/litert/litert-runtime/SKILL.md

Read pinned source

Source commit 6daa53ef27fa2aa87827d183a85c06cedceca833

What was checked

Matches the observed repository HEAD
Yes
Scanner rules are current
Yes
Repository scan complete
No
Skill scan complete
No
Observed behavior
Network or broad shell
Last checked
Nov 3, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings)
10,405
Repository last updated
Nov 5, 2026
License
Apache-2.0

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

Declared tags are partial or unavailable.

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files
Read excerpt

This skill provides step-by-step guidance for building an Android app that runs
a `.tflite` model with the [LiteRT CompiledModel API](https://ai.google.dev/edge/litert/android)
(`com.google.ai.edge.litert:litert` 2.x; [source repository](https://github.com/google-ai-edge/litert/blob/main/README.md)) on the CPU or
the GPU, with the runtime bundled in the APK or from Google Play services. Don't
use the deprecated `org.tensorflow.lite.Interpreter` API. For
open text language models using `.litertlm` files, suggest the `litert-lm`
skill.

## Prerequisites

- A Kotlin Android project (`minSdk` must be `24` or higher, as declared by the LiteRT 2.3.0 AARs).
- The runtime that Step 1 picks: the Google Play services runtime (`com.google.android.gms:play-services-litert`, the default; see [LiteRT in Google Play services](references/play-services.md)), or the bundled runtime (`implementation("com.google.ai.edge.litert:litert:2.3.0")` and `implementation("com.google.ai.edge.litert:litert-gpu:2.3.0")` from Google Maven). For the bundled runtime, from 2.3.0 the GPU accelerator is the second artifact: with `litert` alone the app builds, and `CompiledModel.create` with `CompiledModel.Options(Accelerator.GPU)` throws `LiteRtException` because the APK has no GPU accelerator library. With `CompiledModel.Options(Accelerator.GPU, Accelerator.CPU)` it doesn't throw: the model runs on the CPU while the app reports the GPU, and only Logcat shows it (`GPU accelerator could not be loaded and registered`). No manifest entry is needed: `litert-api`, which `litert` depends on, declares the OpenCL driver libraries (`uses-native-library`) in its own manifest.
- A verified `.tflite` model and its input contract (input dimensions, mean, standard deviation, channel order, and tensor layout). For models with a verified recipe, inspect the [LiteRT model recipes](https://github.com/google-ai-edge/litert-samples/blob/main/models/README.md).