embreex
Python binding for Intel's Embree ray engine
Decision gist · record as of 2026-08-14
Yes, if you need ray tracing in Python and want to avoid compilation overhead. The pre-built wheels, active maintenance, minimal dependency footprint (numpy only), and lack of known vulnerabilities make it a practical choice. Verify the license treatment and API surface match your use case before committing to production.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.8; compiled wheels available for Python 3.8 and newer on major platforms.
- Medium install friction due to compiled wheels, but mitigated by pre-built binaries across macOS, Windows, and Linux.
- Active maintenance with recent release activity and ongoing repository development.
License · maintenance · safety
(unclear) — BSD 2-Clause license (copyright 2015 Anthony Scopatz). License treatment is marked unclear in the fact sheet, so verify the exact licensing terms apply to your use case before depending on this in production.
last release 2026-04-22 (114 days) · last repo commit 2026-07-28 · 27 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,878,729 downloads/mo, #2,846 on PyPI
Alternatives
Verify before relying
pip install embreex
import embreex
# Use embreex ray tracing functions with numpy arrays- What specific ray tracing operations (intersection, occlusion, closest-hit) are exposed by the API.
- Whether the package is actively maintained or in maintenance mode given the 114-day gap since last release.
- Performance characteristics compared to alternative embree bindings.
What it is and what it does
embreex is a fork of pyembree that packages Intel's Embree ray tracing engine as pre-built Python wheels. It was created to meet trimesh's dependency requirements—specifically to provide ray tracing without requiring compilation on Windows, Linux, or Intel macOS for Python 3.6 and newer. The package wraps Embree's ray-geometry intersection algorithms and depends only on numpy.
The project is actively maintained and provides wheels across macOS, Windows, and Linux. It exists alongside other embree bindings but distinguishes itself through its focus on zero-compilation installation and minimal dependencies.
Use it for
- Accelerate ray-casting operations in 3D mesh processing pipelines that use trimesh or similar geometry libraries.
- Compute ray-geometry intersections for physics simulations, collision detection, or visibility queries.
- Build ray tracing applications in Python without managing separate C++ compilation or complex build dependencies.
- Integrate fast ray intersection queries into scientific computing workflows that already use numpy.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need ray tracing in Python and want to avoid compilation overhead.
The pre-built wheels, active maintenance, minimal dependency footprint (numpy only), and lack of known vulnerabilities make it a practical choice. Verify the license treatment and API surface match your use case before committing to production.
Install
embreex on PyPI
Before you install
Medium install friction due to compiled wheels, but mitigated by pre-built binaries across macOS, Windows, and Linux. Active maintenance with recent release activity and ongoing repository development.
Requires Python >=3.8; compiled wheels available for Python 3.8 and newer on major platforms.
License in practice
BSD 2-Clause license (copyright 2015 Anthony Scopatz). License treatment is marked unclear in the fact sheet, so verify the exact licensing terms apply to your use case before depending on this in production.
Quickstart
pip install embreex
import embreex
# Use embreex ray tracing functions with numpy arraysVerify before relying
- What specific ray tracing operations (intersection, occlusion, closest-hit) are exposed by the API.
- Whether the package is actively maintained or in maintenance mode given the 114-day gap since last release.
- Performance characteristics compared to alternative embree bindings.
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagenumpy |
| Maintenance | Actively maintained 114 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 2,878,729 / month, #2,846 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: embreex-4.4.0-cp310-cp310-macosx_13_0_arm64.whl; embreex-4.4.0-cp310-cp310-macosx_13_0_x86_64.whl; embreex-4.4.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; embreex-4.4.0-cp310-cp310-win_amd64.whl; embreex-4.4.0-cp311-cp311-macosx_13_0_arm64.whl; embreex-4.4.0-cp311-cp311-macosx_13_0_x86_64.whl; embreex-4.4.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; embreex-4.4.0-cp311-cp311-win_amd64.whl; embreex-4.4.0-cp312-cp312-macosx_13_0_arm64.whl; embreex-4.4.0-cp312-cp312-macosx_13_0_x86_64.whl; embreex-4.4.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; embreex-4.4.0-cp312-cp312-win_amd64.whl; embreex-4.4.0-cp313-cp313-macosx_13_0_arm64.whl; embreex-4.4.0-cp313-cp313-macosx_13_0_x86_64.whl; embreex-4.4.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; embreex-4.4.0-cp313-cp313-win_amd64.whl; embreex-4.4.0-cp314-cp314-macosx_13_0_arm64.whl; embreex-4.4.0-cp314-cp314-macosx_13_0_x86_64.whl; embreex-4.4.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; embreex-4.4.0-cp314-cp314t-macosx_13_0_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 › “ray tracing python”
- embreexembreex provides Python bindings to Intel's Embree ray tracing…
- mitsubaMitsuba 3 is a research-oriented rendering system that simulates…
- raydpRayDP runs Apache Spark on Ray and integrates Spark with AI…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
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.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also cibuildwheel · trimesh · cascadio · rdkit-pypi · ray · ray-haproxy · xatlas · mitsuba · xgboost-ray · genicam