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An installation and dependency system for Python

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Simple is better than complex - The Zen of Python

Pyflow streamlines working with Python projects and files. It's an easy-to-use CLI app with a minimalist API. Never worry about having the right version of Python or dependencies.

Example use, including setting up a project and switching Py versions: Demonstration

If your project's already configured, the only command you need is

, or
; setting up Python and its dependencies are automatic.

Goals: Make using and publishing Python projects as simple as possible. Actively managing Python environments shouldn't be required to use dependencies safely. We're attempting to fix each stumbling block in the Python workflow, so that it's as elegant as the language itself.

You don't need Python or any other tools installed to use Pyflow.

It runs standalone scripts in their own environments with no config, and project functions directly from the CLI.

It implements PEP 582 -- Python local packages directory and Pep 518 (pyproject.toml).


  • Windows - Download and run this installer. Or, if you have Scoop installed, run

    scoop install pyflow
  • Ubuntu, or another Os that uses Snap - Run

    snap install pyflow --classic
  • Ubuntu or Debian without Snap - Download and run this deb.

  • Fedora, CentOs, RedHat, or older versions of SUSE - Download and run this rpm.

  • A different Linux distro - Download this standalone binary and place it somewhere accessible by the PATH. For example,

  • Mac - Download this zipped Mac binary , ance place the file in it somewhere accessible by the PATH. (Props to @russeldavis for building this)

  • With Pip - Run

    pip install pyflow
    . The linux install using this method is much larger than with the above ones, and it doesn't yet work with Mac. This method will likely not work with Red Hat, CentOs, or Fedora.
    • If you have Rust installed - Run
      cargo install pyflow


  • (Optional) Run
    pyflow init
    in an existing project folder, or
    pyflow new projname
    to create a new project folder.
    imports data from
    creates a folder with the basics.
  • Run
    pyflow install requests
    etc to install packages. Alternatively, edit
  • Run
    to run Python.

Quick-and-dirty start for quick-and-dirty scripts

  • Add the line
    __requires__ = ['numpy', 'requests']
    somewhere in your script, where
    are dependencies. Run
    pyflow script
    , where
    is the name of your script. This will set up an isolated environment for this script, and install dependencies as required. This is a safe way to run one-off Python files that aren't attached to a project, but have dependencies.

Why add another Python manager?

, and
address parts of Pyflow's raison d'être, but expose stumbling blocks that may frustrate new users, both when installing and using. Some reasons why this is different:
  • It behaves consistently regardless of how your system and Python installations are configured.

  • It automatically manages Python installations and environments. You specify a Python version in

    (if omitted, it asks), and it ensures that version is used. If the version's not installed, Pyflow downloads a binary, and uses that. If multiple installations are found for that version, it asks which to use.
    can be used to install Python, but only if your system is configured in a certain way: I don’t think expecting a user’s computer to compile Python is reasonable.
  • By not using Python to install or run, it remains environment-agnostic. This is important for making setup and use as simple and decision-free as possible. It's common for Python-based CLI tools to not run properly when installed from

    due to the
    or user directories not being configured in the expected way.
  • Its dependency resolution and locking is faster due to using a cached database of dependencies, vice downloading and checking each package, or relying on the incomplete data available on the pypi warehouse.

    ’s resolution in particular may be prohibitively-slow on weak internet connections.
  • It keeps dependencies in the project directory, in

    . This is subtle, but reinforces the idea that there's no hidden state.
  • It will always use the specified version of Python. This is a notable limitation in

    ; Poetry may pick the wrong installation (eg Python2 vice Python3), with no obvious way to change it. Poetry allows projects to specify version, but neither selects, nor provides a way to select the right one. If it chooses the wrong one, it will install the wrong environment, and produce a confusing error message. This can be worked around using
    , but this solution isn't documented, and adds friction to the workflow. It may confuse new users, as it occurs by default on popular linux distros like Ubuntu. Additionally,
    docs are confusing: It's not obvious how to install it, what operating systems it's compatible with, or what additional dependencies are required.
  • Multiple versions of a dependency can be installed, allowing resolution of conflicting sub-dependencies. (ie: Your package requires

    Dep A>=1.0
    Dep B
    Dep B
    requires Dep
    ) There are many cases where
    will fail to resolve dependencies. Try it for yourself with a few random dependencies from pypi; there's a good chance you'll hit this problem using
    . Limitations: This will not work for some compiled dependencies, and attempting to package something using this will trigger an error.

Perhaps the biggest philosophical difference is that Pyflow abstracts over environments, rather than expecting users to manage them.

My OS comes with Python, and Virtual environments are easy. What's the point of this?

Hopefully we're not replacing one problem with another.

Some people like the virtual-environment workflow - it requires only tools included with Python, and uses few console commands to create, and activate and environments. However, it may be tedious depending on workflow: The commands may be long depending on the path of virtual envs and projects, and it requires modifying the state of the terminal for each project, each time you use it, which you may find inconvenient or inelegant.

I think we can do better. This is especially relevant for new Python users who don't understand venvs, or are unaware of the hazards of working with a system Python.

improves the workflow by automating environment use, and allowing reproducible dependency graphs.
improves upon
API, speed, and dependency resolution, as well as improving the packaging and distributing process by using a consolidating project config. Both are sensitive to the environment they run in, and won't work correctly if it's not as expected.

addresses these problems elegantly, but maintains a separate repository of binaries from
. If all packages you need are available on
, it may be the best solution. If not, it requires falling back to
, which means using two separate package managers.

When building and deploying packages, a set of overlapping files are traditionally used:
. We use
as the single-source of project info required to build and publish.

A thoroughly biased feature table

These tools have different scopes and purposes:

| Name | Pip + venv | Pipenv | Poetry | pyenv | pythonloc | Conda |this | |------|------------|--------|--------|-------|-----------|-------|-----| | Manages dependencies | ✓ | ✓ | ✓ | | | ✓ | ✓| | Resolves/locks deps | | ✓ | ✓ | | | ✓ | ✓| | Manages Python installations | | | | ✓ | | ✓ | ✓ | | Py-environment-agnostic | | | | ✓ | | ✓ | ✓ | | Included with Python | ✓ | | | | | | | | Stores deps with project | | |✓| | ✓ | | ✓| | Requires changing session state | ✓ | | | ✓ | | | | | Clean build/publish flow | | | ✓ | | | | ✓ | | Supports old Python versions | with

| ✓ | ✓ | ✓ | ✓ | ✓ | | | Isolated envs for scripts | | | | | | | ✓ | | Runs project fns from CLI | | ✓ | ✓ | | | | ✓ |


  • Optionally, create a
    file in your project directory. Otherwise, this file will be created automatically. You may wish to use
    pyflow new
    to create a basic project folder (With a .gitignore, source directory etc), or
    pyflow init
    to populate info from
    . See PEP 518 for details.

Example contents: ```toml [tool.pyflow] py_version = "3.7" name = "runcible" version = "0.2.9" authors = ["John Hackworth [email protected]"]

[tool.pyflow.dependencies] numpy = "^1.16.4" diffeqpy = "1.1.0" ``

section is used for metadata. The only required item in it is
, unless
building and distributing a package. The
contains all dependencies, and is an analog to
. You can specify
developer dependencies in the
section. These
won't be packed or published, but will be installed locally. You can install these
from the cli using the
flag. Eg:
pyflow install black --dev`

You can specify

dependencies, which will only be installed when passing explicit flags to
pyflow install
, or when included in another project with the appropriate flag enabled. Ie packages requiring this one can enable with
pip install -e
test = ["pytest", "nose"]
secure = ["crypto"]

If you'd like to an install a dependency with extras, use syntax like this:

ipython = { version = "^7.7.0", extras = ["qtconsole"] }

To install from a local path instead of

, use syntax like this: ```toml [tool.pyflow.dependencies]

packagename = { path = "path-to-package"}

numpy = { path = "../numpy" } ```

To install from a

repo, use syntax like this:
saturn = { git = "" }  # The trailing `.git` here is optional.

dependencies are currently experimental. If you run into problems with them, please submit an issue.

To install a package that includes a

in its name, enclose the name in quotes.

For details on how to specify dependencies in this

-inspired semver format, reference this guide.

We also attempt to parse metadata and dependencies from tool.poetry sections of

, so there's no need to modify the format if you're using that.

You can specify direct entry points to parts of your program using something like this in

name = "module:function"
Where you replace
, and
with the name to call your script with, the function you wish to run, and the module it's in respectively. This is similar to specifying scripts in
for built packages. The key difference is that functions specified here can be run at any time, without having to build the package. Run with
pyflow name
to do this.

If you run

pyflow package
on on a package using this, the result will work like normal script entry points for someone using the package, regardless of if they're using this tool.

What you can do

Managing dependencies:

  • pyflow install
    - Install all packages in
    , and remove ones not (recursively) specified. If an environment isn't already set up for the version specified in
    , sets one up. Note that this command isn't required to sync dependencies; any relevant
    command will do so automatically.
  • pyflow install requests
    - If you specify one or more packages after
    , those packages will be added to
    and installed. You can use the
    flag to install dev dependencies. eg:
    pyflow install black --dev
  • pyflow install numpy==1.16.4 matplotlib>=3.1
    - Example with multiple dependencies, and specified versions
  • pyflow uninstall requests
    - Remove one or more dependencies

Running REPL and Python files in the environment:

  • pyflow
    - Run a Python REPL
  • pyflow
    - Run a python file
  • pyflow ipython
    pyflow black
    etc - Run a CLI tool like
    , or a project function For the former, this must have been installed by a dependency; for the latter, it's specfied under
  • pyflow script
    - Run a one-off script, outside a project directory, with per-file package management

Building and publishing:

  • pyflow package
    - Package for distribution (uses setuptools internally, and builds both source and wheel.)
  • pyflow package --extras "test all"
    - Package for distribution with extra features enabled, as defined in
  • pyflow publish
    - Upload to PyPi (Repo specified in
    . Uses


  • pyflow list
    - Display all installed packages and console scripts
  • pyflow new projname
    - Create a directory containing the basics for a project: a readme, pyproject.toml, .gitignore, and directory for code
  • pyflow init
    - Create a
    file in an existing project directory. Pull info from
    as required.
  • pyflow reset
    - Remove the environment, and uninstall all packages
  • pyflow clear
    - Clear the cache, of downloaded dependencies, Python installations, or script- environments; it will ask you which ones you'd like to clear.
  • pyflow -V
    - Get the current version of this tool
  • pyflow help
    Get help, including a list of available commands

How installation and locking work


pyflow install
syncs the project's installed dependencies with those specified in
. It generates
, which on subsequent runs, keeps dependencies each package a fixed version, as long as it continues to meet the constraints specified in
. Adding a package name via the CLI, eg
pyflow install matplotlib
simply adds that requirement before proceeding.
isn't meant to be edited directly.

Each dependency listed in

is checked for a compatible match in
If a constraint is met by something in the lock file, the version we'll sync will match that listed in the lock file. If not met, a new entry is added to the lock file, containing the highest version allowed by
. Once complete, packages are installed and removed in order to exactly meet those listed in the updated lock file.

This tool downloads and unpacks wheels from

, or builds wheels from source if none are available. It verifies the integrity of the downloaded file against that listed on
, and the exact versions used are stored in a lock file.

When a dependency is removed from

, it, and its subdependencies not also required by other packages are removed from the

How dependencies are resolved

Compatible versions of dependencies are determined using info from the PyPi Warehouse (available versions, and hash info), and the

database. We use
, which is built specifically for this project, due to inconsistent dependency information stored on
. A dependency graph is built using this cached database. We attempt to use the newest compatible version of each package.

If all packages are either only specified once, or specified multiple times with the same newest-compatible version, we're done resolving, and ready to install and sync.

If a package is included more than once with different newest-compatible versions, but one of those newest-compatible is compatible with all requirements, we install that one. If not, we search all versions to find one that's compatible.

If still unable to find a version of a package that satisfies all requirements, we install multiple versions of it as-required, store them in separate directories, and modify their parents' imports as required.

Note that it may be possible to resolve dependencies in cases not listed above, instead of installing multiple versions. Ie we could try different combinations of top-level packages, check for resolutions, then vary children as-required down the hierarchy. We don't do this because it's slow, has no guarantee of success, and involves installing older versions of packages.


  • Installing global CLI tools
  • The lock file is missing some info like hashes
  • Adding a dependency via the CLI with a specific version constraint, or extras.
  • Install packages from a local
    directly. In the meanwhile, you can use a
    dependency of the unpacked wheel.
  • Dealing with multiple-installed-versions of a dependency that uses importlib or dynamic imports
  • Install Python on Mac

Building and uploading your project to PyPi

In order to build and publish your project, additional info is needed in

, that mimics what would be in
. Example: ```toml [tool.pyflow] name = "everythingkiller" pyversion = "3.6" version = "0.2.9" authors = ["Fraa Erasmas [email protected]"] description = "Small, but packs a punch!" homepage = "https://everything.math" repository = "" license = "MIT" keywords = ["nanotech", "weapons"] classifiers = [ "Topic :: System :: Hardware", "Topic :: Scientific/Engineering :: Human Machine Interfaces", ] pythonrequires = ">=3.6"

If not included, will default to

package_url = ""


name = "module:function"

activate = "jeejah:activate"

[tool.pyflow.dependencies] numpy = "^1.16.4" manimlib = "0.2.9" ipython = {version = "^7.7.0", extras=["qtconsole"]}

[] black = "^18.0" ``

is used to determine which package repository to upload to. If omitted,
Pypi test
is used (`).

Other items you can specify in

: -
: The readme filename, use this if it's named something other than
. -
: A python script to execute building non-python extensions when running
pyflow package

Building this from source

If you’d like to build from source, download and install Rust, clone the repo, and in the repo directory, run

cargo build --release

Ie on linux or Mac:

curl -sSf | sh
git clone
cd pyflow
cargo build --release


  • If installed via
    , run
    scoop update pyflow
  • If installed via
    , run
    snap refresh pyflow
  • If installed via
    , run
    cargo install pyflow --force
  • If installed via
    , run
    pip install --upgrade pyflow
  • If using an installer or deb, run the new version's installer or deb. If manually calling a binary, replace it.


  • If installed via
    , run
    scoop uninstall pyflow
  • If installed via
    , run
    snap remove pyflow
  • If installed via
    , run
    cargo uninstall pyflow
  • If installed via
    , run
    pip uninstall pyflow
  • If installed via Windows installer, run the Installer again and select
    when asked, or use
    Apps & features
  • If installed via a
    , useg the
    Software Center
  • If manually calling a binary, remove it.


If you notice unexpected behavior or missing features, please post an issue, or submit a PR. If you see unexpected behavior, it's probably a bug! Post an issue listing the dependencies that did not install correctly.

Why not to use this

  • It's adding another tool to an already complex field.
  • Most of the features here are already provided by a range of existing packages, like the ones in the table above.
  • The field of contributors is expected to be small, since it's written in a different language.
  • Dependency managers like Pipenv and Poetry work well enough for many cases, have dedicated dev teams, and large userbases.
  • Conda
    in particular handles many things this does quite well.

Dependency cache repo:

  • Github Example API calls:
    . This pulls all top-level dependencies for the
    package, and the dependencies for version
    respectively. There is also a
    API for pulling info on specified versions. The first time this command is run for a package/version combo, it may be slow. Subsequent calls, by anyone, should be fast. This is due to having to download and install each package on the server to properly determine dependencies, due to unreliable information on the
    pypi warehouse

Python binary sources:

Repo binaries are downloaded from

  • Windows: Python official Visual Studio package, by Steve Dower.
  • Newer linux distros: Built on Ubuntu 18.04, using standard procedures.
  • Older linux distros: Built on CentOS 7, using standard procedures.


  • Make sure
    is in your
  • You may need to set up IDEs to find packages in
    . If using PyCharm:
    Project Interpreter
    Show All...
    → (Select the interpreter, ie
    on Linux/Mac, or
    on Windows) → Click the folder-tree icon at the bottom of the pop-out window → Click the
    icon at the bottom of the new pop-out window → Navigate to and select
  • If using VsCode:
    → search
    python extra paths
    Edit in settings.json
    → Add or modify the line:
    "python.autoComplete.extraPaths": ["(projname)/__pypackages__/3.7/lib"]


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