mediapipe
MediaPipe is the simplest way for researchers and developers to build world-class ML solutions and applications for mobile, edge, cloud and the web. See the privacy notice at https://goo.gle/mediapipe-privacy.
Decision gist · record as of 2026-08-14
Yes, if you need on-device ML inference for vision, text, or audio tasks and accept alpha-stage stability. The permissive Apache 2.0 license, active maintenance, and zero known vulnerabilities make it low-risk. Medium install friction (compiled wheels, multiple dependencies) is typical for ML packages. Verify that pre-trained models suit your use case and that you can manage the privacy implications of metrics collection to Google.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a compatible Python version (3.9, 3.10, 3.11, or 3.12) and a pre-trained model file; on-device processing means no internet requirement but models must be available locally.
- Medium install friction due to compiled wheels for multiple platforms (arm64, x86_64, Windows, macOS).
- Active maintenance with recent releases; repo shows 36617 stars and last commit on 2026-08-12.
License · maintenance · safety
Apache 2.0 (permissive) — Apache 2.0 permissive license allows commercial and private use with minimal restrictions. You must include a copy of the license and state significant changes, but can modify and distribute freely.
last release 2026-08-14 (0 days) · last repo commit 2026-08-12 · 36,617 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,648,995 downloads/mo, #2,961 on PyPI
Alternatives
Verify before relying
pip install mediapipe
import mediapipe as mp
from mediapipe.tasks import vision
# Load a pre-built model and run inference
detector = vision.ObjectDetector.create_from_model_path(model_path)- Specific model accuracy benchmarks or latency numbers for different tasks (vision, text, audio).
- Whether legacy solutions mentioned in docs are still functional or fully deprecated.
- Exact hardware requirements or GPU acceleration support details.
- Scope and frequency of metrics collection sent to Google servers.
What it is and what it does
MediaPipe is Google's framework for deploying machine learning models on-device across mobile, web, desktop, and edge platforms. It bundles pre-trained models for common tasks—object detection, pose estimation, hand tracking, face recognition, text classification, and audio analysis—alongside cross-platform APIs to integrate them into applications. All input data (images, video, text, audio) stays on the device; only performance metrics are sent to Google.
The package targets developers and researchers who need to add ML capabilities without building models from scratch or managing cloud inference. It depends on numpy, opencv-contrib-python, matplotlib, sounddevice, flatbuffers, absl-py, and certifi. Installation requires a compatible wheel for your platform (arm64 or x86_64 Linux, macOS 11+, or Windows), and you must supply or download pre-trained model files separately. The framework is actively maintained and in alpha status, with documentation and setup guides available for Python, Android, web, and iOS.
Use it for
- Build real-time pose or hand tracking in mobile apps or desktop applications without cloud inference.
- Add object detection or face recognition to video processing pipelines on edge devices.
- Classify text or audio input on-device for privacy-sensitive applications.
- Prototype ML features quickly using pre-trained models before customizing with MediaPipe Model Maker.
- Deploy ML inference in IoT or embedded systems where latency and privacy are critical.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need on-device ML inference for vision, text, or audio tasks and accept alpha-stage stability.
The permissive Apache 2.0 license, active maintenance, and zero known vulnerabilities make it low-risk. Medium install friction (compiled wheels, multiple dependencies) is typical for ML packages. Verify that pre-trained models suit your use case and that you can manage the privacy implications of metrics collection to Google.
Install
mediapipe on PyPI
Before you install
Medium install friction due to compiled wheels for multiple platforms (arm64, x86_64, Windows, macOS). Active maintenance with recent releases; repo shows 36617 stars and last commit on 2026-08-12. Seven runtime dependencies including numpy, opencv-contrib-python, and matplotlib add to setup complexity.
Requires a compatible Python version (3.9, 3.10, 3.11, or 3.12) and a pre-trained model file; on-device processing means no internet requirement but models must be available locally.
License in practice
Apache 2.0 permissive license allows commercial and private use with minimal restrictions. You must include a copy of the license and state significant changes, but can modify and distribute freely.
Quickstart
pip install mediapipe
import mediapipe as mp
from mediapipe.tasks import vision
# Load a pre-built model and run inference
detector = vision.ObjectDetector.create_from_model_path(model_path)Verify before relying
- Specific model accuracy benchmarks or latency numbers for different tasks (vision, text, audio).
- Whether legacy solutions mentioned in docs are still functional or fully deprecated.
- Exact hardware requirements or GPU acceleration support details.
- Scope and frequency of metrics collection sent to Google servers.
Package facts
| License | Apache 2.0 permissive |
| Python support | Not specified |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 7 packagesabsl-pycertifinumpysounddeviceflatbuffersopencv-contrib-pythonmatplotlib |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 2,648,995 / month, #2,961 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: MacOS :: MacOS XOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: mediapipe-1.0.1-py3-none-macosx_11_0_arm64.whl; mediapipe-1.0.1-py3-none-manylinux_2_28_aarch64.whl; mediapipe-1.0.1-py3-none-manylinux_2_28_x86_64.whl; mediapipe-1.0.1-py3-none-win_amd64.whl; mediapipe-1.0.1-py3-none-win_arm64.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “on-device machine learning models”
- mediapipeMediaPipe provides pre-built machine learning models and…
- tflite-runtimeTensorFlow Lite runtime enables on-device machine learning inference…
- ai-edge-litert-nightlyLiteRT is a runtime for running trained machine learning models on…
Give your agent the search over MCP, or paste the wish link into any chat.
More Software Development packages
Provides backported and experimental type hints for Python 3.9+, allowing use of newer typing features on older Python versions and enabling early experimentation with type system PEPs before they enter the standard library.
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
FastAPI is a Python web framework for building REST APIs using type hints, with automatic request validation, serialization, and interactive API documentation.
Provides a way to document function parameters, class attributes, return types, and variables inline using Python's `Annotated` type hint syntax instead of traditional docstrings.
Typer builds command-line applications from Python functions using type hints, automatically generating help text, argument parsing, and shell completion.
Install it if you are building CLIs in Python.
Distlib provides low-level packaging utilities for building, distributing, and managing Python software—including metadata handling, version specifiers, wheel support, script installation, and dependency resolution.
See also ai-edge-litert · tflite-runtime · executorch · litert-converter · mnn · ai-edge-litert-nightly · mmdet · litert-lm-builder · pipecat-ai · pytorchcv