This section of the documentation is currently being written. pip developers welcome your help to complete this documentation. If you’re interested in helping out, please let us know in the tracking issue, or just submit a pull request and mention it in that tracking issue.

Issue Triage

This serves as an introduction to issue tracking in pip as well as how to help triage reported issues.

Issue Tracker

The pip issue tracker is hosted on GitHub alongside the project.

Currently, the issue tracker is used for bugs, feature requests, and general user support.

In the pip issue tracker, we make use of labels and milestones to organize and track work.


Issue labels are used to:

  1. Categorize issues

  2. Provide status information for contributors and reporters

  3. Help contributors find tasks to work on

The current set of labels are divided into several categories identified by prefix:

C - Category

which area of pip functionality a feature request or issue is related to

K - Kind O - Operating System : for issues that are OS-specific

P - Project/Platform

related to something external to pip

R - Resolution

no more discussion is really needed, an action has been identified and the issue is waiting or closed

S - State

for some automatic labels and other indicators that work is needed


the role or flavor of an issue

The specific labels falling into each category have a description that can be seen on the Labels page.

In addition, there are several standalone labels:

good first issue

this label marks an issue as beginner-friendly and shows up in banners that GitHub displays for first-time visitors to the repository


default label given to issues when they are created


special label for pull requests that removes the news file requirement

needs rebase or merge

this is a special label used by BrownTruck to mark PRs that have merge conflicts


There are several helpers to manage issues and pull requests.

Issues created on the issue tracker are automatically given the triage label by the triage-new-issues bot. The label is automatically removed when another label is added.

When an issue needs feedback from the author we can label it with S: awaiting response. When the author responds, the no-response bot removes the label.

After an issue has been closed for 30 days, the lock bot locks the issue and adds the S: auto-locked label. This allows us to avoid monitoring existing closed issues, but unfortunately prevents and references to issues from showing up as links on the closed issue.

Triage Issues

Users can make issues for a number of reasons:

  1. Suggestions about pip features that could be added or improved

  2. Problems using pip

  3. Concerns about pip usability

  4. General packaging problems to be solved with pip

  5. Problems installing or using Python packages

  6. Problems managing virtual environments

  7. Problems managing Python installations

To triage issues means to identify what kind of issue is happening and

  • confirm bugs

  • provide support

  • discuss and design around the uses of the tool

Specifically, to address an issue:

  1. Read issue title

  2. Scan issue description

  3. Ask questions

  4. If time is available, try to reproduce

  5. Search for or remember related issues and link to them

  6. Identify an appropriate area of concern (if applicable)

Keep in mind that all communication is happening with other people and should be done with respect per the Code of Conduct.

The lifecycle of an issue (bug or support) generally looks like:

  1. waiting for triage (marked with label triage)

  2. confirming issue - some discussion with the user, gathering details, trying to reproduce the issue (may be marked with a specific category, S: awaiting-response, S: discussion-needed, or S: need-repro)

  3. confirmed - the issue is pretty consistently reproducible in a straightforward way, or a mechanism that could be causing the issue has been identified

  4. awaiting fix - the fix is identified and no real discussion on the issue is needed, should be marked R: awaiting PR

  5. closed - can be for several reasons

    • fixed

    • could not be reproduced, no more details could be obtained, and no progress can be made

    • actual issue was with another project or related to system configuration and pip cannot (or will not) be adapted for it

Requesting information

Requesting more information to better understand the context and environment that led to the issue. Examples of specific information that may be useful depending on the situation:

  • pip debug: pip debug

  • pip version: pip -V

  • Python version: python -VV

  • Python path: python -c 'import sys; print(sys.executable)'

  • python on PATH: Unix: which python; Windows: where python

  • Python as resolved by the shell: type python

  • Origin of pip (, OS-level package manager, ensurepip, manual installation)

  • Using a virtual environment (with --system-site-packages?)

  • Using a conda environment

  • PATH environment variable

  • Network situation (e.g. airgapped environment, firewalls)

  • --verbose output of a failing command

  • (Unix) strace output from a failing command (be careful not to output into the same directory as a package that’s being installed, otherwise pip will loop forever copying the log file…)

  • (Windows) procmon output during a failing command (example request)

  • Listing of files relevant to the issue (e.g. ls -l venv/lib/pythonX.Y/problem-package.dist-info/)

  • whether the unexpected behavior ever worked as expected - if so then what were the details of the setup (same information as above)

Generally, information is good to request if it can help confirm or rule out possible sources of error. We shouldn’t request information that does not improve our understanding of the situation.

Reproducing issues

Whenever an issue happens and the cause isn’t obvious, it is important that we be able to reproduce it independently. This serves several purposes:

  1. If it is a pip bug, then any fix will need tests - a good reproducer is most of the way towards that.

  2. If it is not reproducible using the provided instructions, that helps rule out a lot of possible causes.

  3. A clear set of instructions is an easy way to get on the same page as someone reporting an issue.

The best way to reproduce an issue is with a script.

A script can be copied into a file and executed, whereas shell output has to be manually copied a line at a time.

Scripts to reproduce issues should be:

  • portable (few/no assumptions about the system, other that it being Unix or Windows as applicable)

  • non-destructive

  • convenient

  • require little/no setup on the part of the runner


  • creating and installing multiple wheels with different versions (link)

  • using a small web server for authentication errors (link)

  • using docker to test system or global configuration-related issues (link)

  • using docker to test special filesystem permission/configurations (link)

  • using docker for global installation with get-pip (link)

  • get-pip on system with no /usr/lib64 (link)

  • reproducing with pip from current development branch (link)

Reaching resolution

Some user support questions are more related to system configuration than pip. It’s important to treat these issues with the same care and attention as others, specifically:

  1. Unless the issue is very old and the user doesn’t seem active, wait for confirmation before closing the issue

  2. Direct the user to the most appropriate forum for their questions:

  3. Just because a user support question is best solved using some other forum doesn’t mean that we can’t make things easier. Try to extract and understand from the user query how things could have been made easier for them or you, for example with better warning or error messages. If an issue does not exist covering that case then create one. If an issue does exist then make sure to reference that issue before closing this one.

  4. A user may be having trouble installing a package, where the package or build-backend configuration is non-trivial. In these cases we can help to troubleshoot but the best advice is going to be to direct them to the support channels for the related projects.

  5. Do not be hasty to assume it is one cause or another. What looks like someone else’s problem may still be an issue in pip or at least something that could be improved.

  6. For general discussion on Python packaging:

Closing issues

An issue may be considered resolved and closed when:

  • for each possible improvement or problem represented in the issue discussion:

    • Consensus has been reached on a specific action and the actions appear to be external to the project, with no follow up needed in the project afterwards.

      • PEP updates (with a corresponding issue in python/peps)

      • already tracked by another issue

    • A project-specific issue has been identified and the issue no longer occurs as of the latest commit on the main branch.

  • An enhancement or feature request no longer has a proponent and the maintainers don’t think it’s worth keeping open.

  • An issue has been identified as a duplicate, and it is clearly a duplicate (i.e. the original report was very good and points directly to the issue)

  • The issue has been fixed, and can be independently validated as no longer being an issue. If this is with code then the specific change/PR that led to it should be identified and posted for tracking.

Common issues

  1. network-related issues - any issue involving retries, address lookup, or anything like that are typically network issues.

  2. issues related to having multiple Python versions, or an OS package manager-managed pip/python installation (specifically with Debian/Ubuntu). These typically present themselves as:

    1. Not being able to find installed packages

    2. basic libraries not able to be found, fundamental OS components missing

    3. In these situations you will want to make sure that we know how they got their Python and pip. Knowing the relevant package manager commands can help, e.g. dpkg -S.

For issues caused by changes by redistributors

Certain issues are caused by patches that redistributors of Python/pip make to Python/pip.

Certain redistributors have shared preferred wording to redirect users to their issue trackers.

Fedora, RHEL, CentOS (and probably other derivatives – Rocky, Scientific, CloudLinux):

This issue looks like it's caused by changes that Fedora or Red Hat
made in their pip packaging. Please file a Fedora bug at

cc @encukou @hroncok


This issue looks like it's caused by changes that Debian made in
their pip packaging. Please file a bug with Debian, with
`reportbug python3-pip` [Docs](
You can link to this issue in your bug report.

In the meantime, you may be able to work-around your issue by upgrading
pip inside your virtualenv: `python -m pip install -U pip`


This issue looks like it's caused by changes that Ubuntu made in
their pip packaging. Please file a bug with Ubuntu, with
`ubuntu-bug python3-pip` [Docs](
You can link to this issue in your bug report.

In the meantime, you may be able to work-around your issue by upgrading
pip inside your virtualenv: `python -m pip install -U pip`