ai-edge-litert
LiteRT is for mobile and embedded devices.
Decision gist · record as of 2026-08-14
Yes, if you are building a mobile or embedded application and already have a TensorFlow Lite model ready to deploy. The package is actively maintained, permissively licensed, and purpose-built for this use case. Not relevant for model training, conversion, or server-side inference—use TensorFlow or another framework for those tasks.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a compatible TensorFlow Lite model file (.tflite format) and a supported Python version (3.8 or later based on wheel availability).
- Medium install friction due to precompiled wheels for multiple Python versions (3.10–3.14) and platforms (macOS ARM, Linux x86/ARM, Windows).
- Active maintenance with a release 2 days ago.
License · maintenance · safety
Apache 2.0 (permissive) — Apache 2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for production mobile and embedded applications.
last release 2026-08-12 (2 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 768,493 downloads/mo, #5,113 on PyPI
Alternatives
Verify before relying
pip install ai-edge-litert
import ai_edge_litert
# Load and run a model on an edge device
interpreter = ai_edge_litert.Interpreter(model_path='model.tflite')
interpreter.invoke()- Whether the package includes documentation or examples for model conversion and optimization for edge devices.
- Performance benchmarks or latency guarantees on specific mobile/embedded hardware.
- API stability and backward compatibility guarantees across minor versions.
What it is and what it does
ai-edge-litert is Google's official runtime for executing pre-trained machine learning models directly on mobile phones, tablets, and embedded systems. It handles the low-level inference work—loading a model, preparing inputs, and running predictions—with optimizations for minimal memory and power consumption. The package wraps TensorFlow Lite's core runtime and is designed to integrate into Python applications running on edge devices where cloud connectivity or server-side inference is impractical or undesirable.
The package depends on numpy for numerical operations, protobuf and flatbuffers for model serialization, and several utility libraries. Installation is straightforward on modern Python versions (3.8–3.14) across macOS, Linux, and Windows, though you must supply a pre-converted TensorFlow Lite model file. It is intended for developers building inference pipelines on resource-constrained hardware, not for model training or conversion.
Use it for
- Run image classification or object detection on a mobile phone without sending data to a server.
- Deploy a speech recognition or natural language model on an embedded device with limited connectivity.
- Execute real-time pose estimation or gesture recognition on an edge device for interactive applications.
- Integrate a pre-trained recommendation or anomaly detection model into an IoT sensor or gateway.
- Build offline-capable mobile apps that perform inference without internet access.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building a mobile or embedded application and already have a TensorFlow Lite model ready to deploy.
The package is actively maintained, permissively licensed, and purpose-built for this use case. Not relevant for model training, conversion, or server-side inference—use TensorFlow or another framework for those tasks.
Install
ai-edge-litert on PyPI
Before you install
Medium install friction due to precompiled wheels for multiple Python versions (3.10–3.14) and platforms (macOS ARM, Linux x86/ARM, Windows). Active maintenance with a release 2 days ago. Seven runtime dependencies including numpy, protobuf, and flatbuffers add some setup overhead.
Requires a compatible TensorFlow Lite model file (.tflite format) and a supported Python version (3.8 or later based on wheel availability).
License in practice
Apache 2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for production mobile and embedded applications.
Quickstart
pip install ai-edge-litert
import ai_edge_litert
# Load and run a model on an edge device
interpreter = ai_edge_litert.Interpreter(model_path='model.tflite')
interpreter.invoke()Verify before relying
- Whether the package includes documentation or examples for model conversion and optimization for edge devices.
- Performance benchmarks or latency guarantees on specific mobile/embedded hardware.
- API stability and backward compatibility guarantees across minor versions.
Package facts
| License | Apache 2.0 permissive |
| Python support | Not specified |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 7 packagesbackports.strenumflatbuffersnumpytqdmtyping-extensionsprotobufml_dtypes |
| Maintenance | Actively maintained 2 days since the last release |
| First released | |
| Downloads | 768,493 / month, #5,113 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: MathematicsTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: ai_edge_litert-2.2.0-cp310-cp310-macosx_12_0_arm64.whl; ai_edge_litert-2.2.0-cp310-cp310-manylinux_2_27_aarch64.whl; ai_edge_litert-2.2.0-cp310-cp310-manylinux_2_27_x86_64.whl; ai_edge_litert-2.2.0-cp310-cp310-win_amd64.whl; ai_edge_litert-2.2.0-cp311-cp311-macosx_12_0_arm64.whl; ai_edge_litert-2.2.0-cp311-cp311-manylinux_2_27_aarch64.whl; ai_edge_litert-2.2.0-cp311-cp311-manylinux_2_27_x86_64.whl; ai_edge_litert-2.2.0-cp311-cp311-win_amd64.whl; ai_edge_litert-2.2.0-cp312-cp312-macosx_12_0_arm64.whl; ai_edge_litert-2.2.0-cp312-cp312-manylinux_2_27_aarch64.whl; ai_edge_litert-2.2.0-cp312-cp312-manylinux_2_27_x86_64.whl; ai_edge_litert-2.2.0-cp312-cp312-win_amd64.whl; ai_edge_litert-2.2.0-cp313-cp313-macosx_12_0_arm64.whl; ai_edge_litert-2.2.0-cp313-cp313-manylinux_2_27_aarch64.whl; ai_edge_litert-2.2.0-cp313-cp313-manylinux_2_27_x86_64.whl; ai_edge_litert-2.2.0-cp313-cp313-win_amd64.whl; ai_edge_litert-2.2.0-cp314-cp314-macosx_12_0_arm64.whl; ai_edge_litert-2.2.0-cp314-cp314-manylinux_2_27_aarch64.whl; ai_edge_litert-2.2.0-cp314-cp314-manylinux_2_27_x86_64.whl; ai_edge_litert-2.2.0-cp314-cp314-win_amd64.whl
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See also ai-edge-litert-nightly · ai-edge-quantizer · litert-lm-builder · litert-torch · mediapipe · tflite-runtime · mnn · executorch · mobly · litert-converter