{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/6"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/4"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/3"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/8"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/4"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"ai-edge-litert runs machine learning models on mobile and embedded devices with low latency and small binary footprint, supporting Android, iOS, and other operating systems.","skillfed_tags":["edge-inference","mobile-ml","tensorflow-lite"],"use_cases":["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."],"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\u2014loading a model, preparing inputs, and running predictions\u2014with 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.\n\nThe 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\u20133.14) 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.","worth_installing":"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\u2014use TensorFlow or another framework for those tasks."},"id":"ai-edge-litert","links":{"html":"https://skillfed.io/packages/ai-edge-litert","md":"https://skillfed.io/packages/ai-edge-litert.md","pypi":"https://pypi.org/project/ai-edge-litert/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-12","license_spdx":null,"license_treatment":"permissive","name":"ai-edge-litert","python_support":"unspecified","summary":"LiteRT is for mobile and embedded devices."},"popularity":{"monthly_downloads":768493,"position":5113,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.2.0"}