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LiteRT is the new name for TensorFlow Lite’s on-device runtime, not a replacement model format. If you use the classic Interpreter API, the simplest migration is generally to update the package and import while keeping your inference logic and existing .tflite models. LiteRT v2’s CompiledModel API is a separate modernization path that requires adopting a different API.
What changed—and what stayed the same
Google announced the LiteRT name in September 2024 as part of its Google AI Edge suite, reflecting a direction beyond TensorFlow. The announcement said that the name change itself did not require changes to deployed apps, class or method names, or the model format. Package users who want the renamed distribution do need to move to LiteRT packages. Google’s announcement says conversion continues to produce .tflite files and LiteRT reads them.
That format continuity does not establish that every model, operator, device, or delegate behaves identically. Treat compatibility as something to verify in your own app rather than assuming the unchanged extension guarantees universal parity.
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| Old name or component | Current name or action | What it means |
|---|---|---|
| TensorFlow Lite runtime | LiteRT | The runtime’s new name under Google AI Edge. |
Android org.tensorflow:tensorflow-lite |
com.google.ai.edge.litert:litert |
Use the LiteRT Maven artifact family; the migration guide also lists GPU and metadata artifacts. |
Python tflite-runtime |
ai-edge-litert |
The guide’s example imports the Interpreter from ai_edge_litert.interpreter. |
Python tf.lite.Interpreter |
ai_edge_litert.interpreter.Interpreter |
TensorFlow 2.19 announced a deprecation redirect and planned removal in 2.20; check the TensorFlow version you build against. |
.tflite extension and model format |
Unchanged | The announcement says LiteRT continues to read the existing format. |
| LiteRT v1 | Classic TensorFlow Lite Interpreter API |
The low-friction route: package migration without inference-logic changes, according to the guide. |
| LiteRT v2 | CompiledModel API |
A distinct API generation for newer acceleration-oriented workflows. |
| Swift/Objective-C SDKs, C++ SDK, Task Library, Model Maker | Remain in TensorFlow Lite packages | Do not assume these components have a direct LiteRT package swap. |
Names and migration paths above follow Google’s LiteRT migration guide.
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Choose the migration path that fits your app
Keep the classic Interpreter API
Choose this route when you want the smallest change and your application already uses the Interpreter. Update the relevant platform dependency and, for Python, the import to LiteRT’s package. The guide describes this as a package swap that does not require inference-logic changes. Verify the selected artifact and version in the official guide for your platform; no artifact version number is assumed here.
Adopt LiteRT v2 and CompiledModel
Choose this route when you intend to move to the newer API rather than simply rename the dependency. The CompiledModel path is described as supporting accelerator selection, GPU/NPU support, zero-copy buffers, and asynchronous execution. Moving to it means changing the inference API, so it is not just a package update. Those capabilities do not guarantee a speedup on every model or device; benchmark and validate on your target hardware.
What Python users need to know about TensorFlow versions
TensorFlow 2.19 release notes said tf.lite.Interpreter would issue a deprecation warning redirecting users to ai_edge_litert.interpreter, with deletion planned for TensorFlow 2.20. TensorFlow 2.20 release notes describe LiteRT decoupling from TensorFlow and say tf.lite will be removed from future TensorFlow Python packages. These statements are tied to those release notes and versions; check the TensorFlow release you actually install and build against.
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See the TensorFlow 2.19 release notes and TensorFlow 2.20 release notes.
Check associated libraries before changing dependencies
Not every component associated with TensorFlow Lite moves with the runtime rename. Google’s migration guide says the Swift/Objective-C SDKs, C++ SDK, Task Library, and Model Maker remain in TensorFlow Lite packages. If your app depends on one of these, follow that library’s stated package path rather than replacing all TensorFlow Lite references with LiteRT names indiscriminately.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Does the rename affect a production app or its model files?
The September 2024 announcement said the original name change alone did not require changes to deployed applications or to the .tflite format. A project changing packages to use LiteRT still needs to update its dependency and, where applicable, imports. Test the actual app and model on the target devices after changing dependencies; format continuity is not a blanket guarantee of identical runtime behavior.
Google’s announcement also reported that TensorFlow Lite was used by over 100,000 apps and 2.7 billion devices. Those are Google-reported figures from 2024, not independently verified adoption measurements. Read the announcement.
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