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OS-agnostic, system-level binary package manager.

Reason this release was yanked:

Pip installing conda leads to broken UX; please install using miniconda or miniforge instead. See

Project description

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Conda is a cross-platform, Python-agnostic binary package manager. It is the package manager used by Anaconda installations, but it may be used for other systems as well. Conda makes environments first-class citizens, making it easy to create independent environments even for C libraries. Conda is written entirely in Python, and is BSD licensed open source.

Conda is enhanced by organizations, tools, and repositories created and managed by the amazing members of the conda community. Some of them can be found here.


Conda is a part of the Anaconda distribution. You can also download a minimal installation that only includes conda and its dependencies, called Miniconda.

Getting Started

If you install Anaconda, you will already have hundreds of packages installed. You can see what packages are installed by running

$ conda list

to see all the packages that are available, use

$ conda search

and to install a package, use

$ conda install <package-name>

The real power of conda comes from its ability to manage environments. In conda, an environment can be thought of as a completely separate installation. Conda installs packages into environments efficiently using hard links by default when it is possible, so environments are space efficient, and take seconds to create.

The default environment, which conda itself is installed into is called root. To create another environment, use the conda create command. For instance, to create an environment with the IPython notebook and NumPy 1.6, which is older than the version that comes with Anaconda by default, you would run

$ conda create -n numpy16 ipython-notebook numpy=1.6

This creates an environment called numpy16 with the latest version of the IPython notebook, NumPy 1.6, and their dependencies.

We can now activate this environment, use

# On Linux and Mac OS X
$ source activate numpy16

# On Windows
> activate numpy16

This puts the bin directory of the numpy16 environment in the front of the PATH, and sets it as the default environment for all subsequent conda commands.

To go back to the root environment, use

# On Linux and Mac OS X
$ source deactivate

# On Windows
> deactivate

Building Your Own Packages

You can easily build your own packages for conda, and upload them to, a free service for hosting packages for conda, as well as other package managers. To build a package, create a recipe. See for many example recipes, and for documentation on how to build recipes.

To upload to, create an account. Then, install the anaconda-client and login

$ conda install anaconda-client
$ anaconda login

Then, after you build your recipe

$ conda build <recipe-dir>

you will be prompted to upload to

To add your channel, or the channel of others to conda so that conda install will find and install their packages, run

$ conda config --add channels

(replacing username with the user name of the person whose channel you want to add).

Getting Help

The documentation for conda is at You can subscribe to the conda mailing list. The source code and issue tracker for conda are on GitHub.


Contributions to conda are welcome. Just fork the GitHub repository and send a pull request.

To develop on conda, the easiest way is to use python develop in your root conda environment. This will install a link to the local conda source code, so that any change you make to conda will be instantly available. To undo this, run python develop -u. If you are worried about breaking your conda installation, you can install a separate instance of Miniconda and work off it. This is also the only way to test conda in both Python 2 and Python 3, as conda can only be installed into a root environment.

Run the conda tests by conda install pytest and then running py.test in the conda directory. The tests are also run by Travis CI when you make a pull request.

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