OpenMDAO
OpenMDAO is an open-source high-performance computing platform for systems analysis and multidisciplinary optimization, written in Python. It enables you to decompose your models, making them easier to build and maintain, while still solving them in a tightly coupled manner with efficient parallel numerical methods.
The OpenMDAO project is primarily focused on supporting gradient-based optimization with analytic derivatives to allow you to explore large design spaces with hundreds or thousands of design variables, but the framework also has a number of parallel computing features that can work with gradient-free optimization, mixed-integer nonlinear programming, and traditional design space exploration.
If you are using OpenMDAO, please cite us!
Documentation
Documentation for the latest development version can be found here.
Documentation for all released versions can be found here.
Important Notice
While the API is relatively stable, OpenMDAO remains in active development. There will be periodic changes to the API. User's are encouraged to pin their version of OpenMDAO to a recent release and update periodically.
Install OpenMDAO
You have two options for installing OpenMDAO, (1) from the Python Package Index (PyPI), and (2) from the GitHub repository.
OpenMDAO includes several optional sets of dependencies including:
test for installing the developer tools (e.g., testing, coverage),
docs for building the documentation and
visualization for some extra visualization tools.
Specifying all will include all of the optional dependencies.
Install from PyPI
This is the easiest way to install OpenMDAO. To install only the runtime dependencies:
pip install openmdao
To install all the optional dependencies:
pip install openmdao[all]
Install from a Cloned Repository
This allows you to install OpenMDAO from a local copy of the source code.
git clone http://github.com/OpenMDAO/OpenMDAO
cd OpenMDAO
pip install .
If you would like to make changes to OpenMDAO it is recommended you
install it in editable mode (i.e., development mode) by adding the -e
flag when calling pip, this way any changes you make to the source code will
be included when you import OpenMDAO in Python. You will also want to
install the packages necessary for running OpenMDAO's tests and documentation
generator. You can install everything needed for development by running:
pip install -e OpenMDAO[all]
Using Pixi for reproducable environments
Fully utilizing OpenMDAO means relying on a number of third-party dependencies, notably pyoptsparse, mpi4py, petsc4py, numpy, and scipy. Keeping so many dependencies in sync can be a challenge. To help users, OpenMDAO uses pixi to maintain reproduceable environments. The goal of this is to ensure that users of a given OpenMDAO release can reproduce the environment against which that release was tested.
Starting with version 3.42.0, OpenMDAO provides Pixi environments for dependency management. Pixi is especially useful because some OpenMDAO dependencies (like MPI, PETSc) are not available on PyPI but are needed for parallel computing features.
You can read more about the using OpenMDAO's curated Pixi environments in the "getting started" section of our documentation.
OpenMDAO Versions
OpenMDAO 3.x.y represents the current, supported version. It requires Python 3.10 or later and is maintained here. To upgrade to the latest release, run:
pip install --upgrade openmdao
OpenMDAO 2.10.x was the last version to support Python 2.x and is no longer supported. To install this older release, run:
pip install "openmdao<3"
OpenMDAO 1.7.4 was an earlier version of OpenMDAO and is also no longer supported. The code repository is now named OpenMDAO1, and has moved here. To install it, run:
pip install "openmdao<2"
The legacy OpenMDAO v0.x (versions 0.13.0 and older) of the OpenMDAO-Framework are here.
Test OpenMDAO
Users are encouraged to run the unit tests to ensure OpenMDAO is performing correctly. In order to do so, you must install the testing dependencies.
-
Install OpenMDAO and its testing dependencies:
pip install openmdao[test]Alternatively, you can clone the repository, as explained here, and install the development dependencies as described here.
-
Run tests:
testflo openmdao -n 1 -
If everything works correctly, you should see a message stating that there were zero failures. If the tests produce failures, you are encouraged to report them as an issue. If so, please make sure you include your system spec, and include the error message.
If tests fail, please include your system information, you can obtain that by running the following commands in python and copying the results produced by the last line.
import platform, sys info = platform.uname() (info.system, info.version), (info.machine, info.processor), sys.versionWhich should produce a result similar to:
(('Windows', '10.0.17134'), ('AMD64', 'Intel64 Family 6 Model 94 Stepping 3, GenuineIntel'), '3.6.6 | packaged by conda-forge | (default, Jul 26 2018, 11:48:23) ...')
Build the Documentation for OpenMDAO
Documentation for the latest version can always be found here, but if you would like to build a local copy you can find instructions to do so here.
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Release files for openmdao 3.45.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| openmdao-3.45.1.tar.gz | 13.2 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| openmdao-3.45.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 27.1 MB
Release files / openmdao-3.45.1.tar.gz
| Download URL | openmdao-3.45.1.tar.gz |
|---|---|
| Size | 13.2 MB |
| Tags | Source |
|
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
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Transparency logRelease files / openmdao-3.45.1-py3-none-any.whl
| Download URL | openmdao-3.45.1-py3-none-any.whl |
|---|---|
| Size | 13.9 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 11, 2026.
Transparency log
