Installation from PyPI

This page describes installations using the apache-airflow package published in PyPI.

Installation tools

Only pip installation is currently officially supported.


While there are some successes with using other tools like poetry or pip-tools, they do not share the same workflow as pip - especially when it comes to constraint vs. requirements management. Installing via Poetry or pip-tools is not currently supported. If you wish to install airflow using those tools you should use the constraints and convert them to appropriate format and workflow that your tool requires.

There are known issues with bazel that might lead to circular dependencies when using it to install Airflow. Please switch to pip if you encounter such problems. Bazel community works on fixing the problem in this PR so it might be that newer versions of bazel will handle it.

Typical command to install airflow from scratch in a reproducible way from PyPI looks like below:

pip install "apache-airflow[celery]==2.10.0.dev0" --constraint ""

Typically, you can add other dependencies and providers as separate command after the reproducible installation - this way you can upgrade or downgrade the dependencies as you see fit, without limiting them to constraints. Good practice for those is to extend such pip install command with the apache-airflow pinned to the version you have already installed to make sure it is not accidentally upgraded or downgraded by pip.

pip install "apache-airflow==2.10.0.dev0" apache-airflow-providers-google==10.1.0

Those are just examples, see further for more explanation why those are the best practices.


Generally speaking, Python community established practice is to perform application installation in a virtualenv created with virtualenv or venv tools. You can also use pipx to install Airflow® in a application dedicated virtual environment created for you. There are also other tools that can be used to manage your virtualenv installation and you are free to choose how you are managing the environments. Airflow has no limitation regarding to the tool of your choice when it comes to virtual environment.

The only exception where you might consider not using virtualenv is when you are building a container image with only Airflow installed - this is for example how Airflow is installed in the official Container image.

Constraints files

Why we need constraints

Airflow® installation can be tricky because Airflow is both a library and an application.

Libraries usually keep their dependencies open and applications usually pin them, but we should do neither and both at the same time. We decided to keep our dependencies as open as possible (in pyproject.toml) so users can install different version of libraries if needed. This means that from time to time plain pip install apache-airflow will not work or will produce an unusable Airflow installation.

Reproducible Airflow installation

In order to have a reproducible installation, we also keep a set of constraint files in the constraints-main, constraints-2-0, constraints-2-1 etc. orphan branches and then we create a tag for each released version e.g. constraints-2.10.0.dev0.

This way, we keep a tested set of dependencies at the moment of release. This provides you with the ability of having the exact same installation of airflow + providers + dependencies as was known to be working at the moment of release - frozen set of dependencies for that version of Airflow. There is a separate constraints file for each version of Python that Airflow supports.

You can create the URL to the file substituting the variables in the template below.${AIRFLOW_VERSION}/constraints-${PYTHON_VERSION}.txt


  • AIRFLOW_VERSION - Airflow version (e.g. 2.10.0.dev0) or main, 2-0, for latest development version

  • PYTHON_VERSION Python version e.g. 3.8, 3.9

The examples below assume that you want to use install airflow in a reproducible way with the celery extra, but you can pick your own set of extras and providers to install.

pip install "apache-airflow[celery]==2.10.0.dev0" --constraint ""


The reproducible installation guarantees that this initial installation steps will always work for you - providing that you use the right Python version and that you have appropriate Operating System dependencies installed for the providers to be installed. Some of the providers require additional OS dependencies to be installed such as build-essential in order to compile libraries, or for example database client libraries in case you install a database provider, etc.. You need to figure out which system dependencies you need when your installation fails and install them before retrying the installation.

Upgrading and installing dependencies (including providers)

The reproducible installation above should not prevent you from being able to upgrade or downgrade providers and other dependencies to other versions

You can, for example, install new versions of providers and dependencies after the release to use the latest version and up-to-date with latest security fixes - even if you do not want upgrade airflow core version. Or you can downgrade some dependencies or providers if you want to keep previous versions for compatibility reasons. Installing such dependencies should be done without constraints as a separate pip command.

When you do such an upgrade, you should make sure to also add the apache-airflow package to the list of packages to install and pin it to the version that you have, otherwise you might end up with a different version of Airflow than you expect because pip can upgrade/downgrade it automatically when performing dependency resolution.

pip install "apache-airflow[celery]==2.10.0.dev0" --constraint ""
pip install "apache-airflow==2.10.0.dev0" apache-airflow-providers-google==10.1.1

You can also downgrade or upgrade other dependencies this way - even if they are not compatible with those dependencies that are stored in the original constraints file:

pip install "apache-airflow[celery]==2.10.0.dev0" --constraint ""
pip install "apache-airflow[celery]==2.10.0.dev0" dbt-core==0.20.0


Not all dependencies can be installed this way - you might have dependencies conflicting with basic requirements of Airflow or other dependencies installed in your system. However, by skipping constraints when you install or upgrade dependencies, you give pip a chance to resolve the conflicts for you, while keeping dependencies within the limits that Apache Airflow, providers and other dependencies require. The resulting combination of those dependencies and the set of dependencies that come with the constraints might not be tested before, but it should work in most cases as we usually add requirements, when Airflow depends on particular versions of some dependencies. In cases you cannot install some dependencies in the same environment as Airflow - you can attempt to use other approaches. See best practices for handling conflicting/complex Python dependencies

Verifying installed dependencies

You can also always run the pip check command to test if the set of your Python packages is consistent and not conflicting.

> pip check
No broken requirements found.

When you see such message and the exit code from pip check is 0, you can be sure, that there are no conflicting dependencies in your environment.

Using your own constraints

When you decide to install your own dependencies, or want to upgrade or downgrade providers, you might want to continue being able to keep reproducible installation of Airflow and those dependencies via a single command. In order to do that, you can produce your own constraints file and use it to install Airflow instead of the one provided by the community.

pip install "apache-airflow[celery]==2.10.0.dev0" --constraint ""
pip install "apache-airflow==2.10.0.dev0" dbt-core==0.20.0
pip freeze > my-constraints.txt

Then you can use it to create reproducible installations of your environment in a single operation via a local constraints file:

pip install "apache-airflow[celery]==2.10.0.dev0" --constraint "my-constraints.txt"

Similarly as in case of Airflow original constraints, you can also host your constraints at your own repository or server and use it remotely from there.

Fixing Constraints at release time

The released “versioned” constraints are mostly fixed when we release Airflow version and we only update them in exceptional circumstances. For example when we find out that the released constraints might prevent Airflow from being installed consistently from the scratch.

In normal circumstances, the constraint files are not going to change if new version of Airflow dependencies are released - not even when those versions contain critical security fixes. The process of Airflow releases is designed around upgrading dependencies automatically where applicable but only when we release a new version of Airflow, not for already released versions.

Between the releases you can upgrade dependencies on your own and you can keep your own constraints updated as described in the previous section.

The easiest way to keep-up with the latest released dependencies is to upgrade to the latest released Airflow version. Whenever we release a new version of Airflow, we upgrade all dependencies to the latest applicable versions and test them together, so if you want to keep up with those tests - staying up-to-date with latest version of Airflow is the easiest way to update those dependencies.

Installation and upgrade scenarios

In order to simplify the installation, we have prepared examples of how to upgrade Airflow and providers.

Installing Airflow® with extras and providers

If you need to install extra dependencies of Airflow®, you can use the script below to make an installation a one-liner (the example below installs Postgres and Google providers, as well as async extra).

PYTHON_VERSION="$(python -c 'import sys; print(f"{sys.version_info.major}.{sys.version_info.minor}")')"
pip install "apache-airflow[async,postgres,google]==${AIRFLOW_VERSION}" --constraint "${CONSTRAINT_URL}"

Note, that it will install the versions of providers that were available at the moment this version of Airflow has been released. You need to run separate pip commands without constraints, if you want to upgrade provider packages in case they were released afterwards.

Upgrading Airflow together with providers

You can upgrade airflow together with extras (providers available at the time of the release of Airflow being installed. This will bring apache-airflow and all providers to the versions that were released and tested together when the version of Airflow you are installing was released.

PYTHON_VERSION="$(python -c 'import sys; print(f"{sys.version_info.major}.{sys.version_info.minor}")')"
pip install "apache-airflow[postgres,google]==${AIRFLOW_VERSION}" --constraint "${CONSTRAINT_URL}"

Managing providers separately from Airflow core

In order to add new features, implement bug-fixes or simply maintain backwards compatibility, you might need to install, upgrade or downgrade any of the providers - separately from the Airflow Core package. We release providers independently from the core of Airflow, so often new versions of providers are released before Airflow is, also if you do not want yet to upgrade Airflow to the latest version, you might want to install just some (or all) newly released providers separately.

As you saw above, when installing the providers separately, you should not use any constraint files.

If you build your environment automatically, You should run provider’s installation as a separate command after Airflow has been installed (usually with constraints). Constraints are only effective during the pip install command they were used with.

It is the best practice to install apache-airflow in the same version as the one that comes from the original image. This way you can be sure that pip will not try to downgrade or upgrade apache airflow while installing other requirements, which might happen in case you try to add a dependency that conflicts with the version of apache-airflow that you are using:

pip install "apache-airflow==2.10.0.dev0" "apache-airflow-providers-google==8.0.0"


Installing, upgrading, downgrading providers separately is not guaranteed to work with all Airflow versions or other providers. Some providers have minimum-required version of Airflow and some versions of providers might have limits on dependencies that are conflicting with limits of other providers or other dependencies installed. For example google provider before 10.1.0 version had limit of protobuf library <=3.20.0 while for example google-ads library that is supported by google has requirement for protobuf library >=4. In such cases installing those two dependencies alongside in a single environment will not work. In such cases you can attempt to use other approaches. See best practices for handling conflicting/complex Python dependencies

Managing just Airflow core without providers

If you don’t want to install any providers you have, just install or upgrade Apache Airflow, you can simply install airflow in the version you need. You can use the special constraints-no-providers constraints file, which is smaller and limits the dependencies to the core of Airflow only, however this can lead to conflicts if your environment already has some of the dependencies installed in different versions and in case you have other providers installed. This command, however, gives you the latest versions of dependencies compatible with just airflow core at the moment Airflow was released.

PYTHON_VERSION="$(python -c 'import sys; print(f"{sys.version_info.major}.{sys.version_info.minor}")')"
# For example: 3.8
# For example:
pip install "apache-airflow==${AIRFLOW_VERSION}" --constraint "${CONSTRAINT_URL}"


Airflow uses Scarf to collect basic usage data during operation. Check the Usage data collection FAQ for more information about the data collected and how to opt-out.


This section describes how to troubleshoot installation issues with PyPI installation.

The ‘airflow’ command is not recognized

If the airflow command is not getting recognized (can happen on Windows when using WSL), then ensure that ~/.local/bin is in your PATH environment variable, and add it in if necessary:


You can also start airflow with python -m airflow

Symbol not found: _Py_GetArgcArgv

If you see Symbol not found: _Py_GetArgcArgv while starting or importing airflow, this may mean that you are using an incompatible version of Python. For a homebrew installed version of Python, this is generally caused by using Python in /usr/local/opt/bin rather than the Frameworks installation (e.g. for python 3.8: /usr/local/opt/python@3.8/Frameworks/Python.framework/Versions/3.8).

The crux of the issue is that a library Airflow depends on, setproctitle, uses a non-public Python API which is not available from the standard installation /usr/local/opt/ (which symlinks to a path under /usr/local/Cellar).

An easy fix is just to ensure you use a version of Python that has a dylib of the Python library available. For example:

# Note: these instructions are for python3.8 but can be loosely modified for other versions
brew install python@3.8
virtualenv -p /usr/local/opt/python@3.8/Frameworks/Python.framework/Versions/3.8/bin/python3 .toy-venv
source .toy-venv/bin/activate
pip install apache-airflow
>>> import setproctitle
# Success!

Alternatively, you can download and install Python directly from the Python website.

Was this entry helpful?