Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given. If you are a new contributor, don’t forget to add yourself to the AUTHORS.rst file in your pull request (either GitHub username, or first/last name).

You can contribute in many ways:

Types of Contributions

Report Bugs

Report bugs at

When reporting a bug, please include:

  • Your operating system name and version.

  • Any details about your local setup that might be helpful in troubleshooting.

  • Detailed steps to reproduce the bug.

Fix Bugs

Look through the GitHub issues for bugs. Anything tagged with “bug” and “help wanted” is open to whomever wants to implement it.

Implement Features

Look through the GitHub issues for features. Anything tagged with “enhancement” and “help wanted” is open to whomever wants to implement it.

Write Documentation

CumulusCI could always use more documentation, whether as part of the official CumulusCI docs, in docstrings, or even on the web in blog posts, articles, and such.

Submit Feedback

The best way to send feedback is to file an issue.

If you are proposing a feature:

  • Explain in detail how it would work.

  • Keep the scope as narrow as possible, to make it easier to implement.

  • Remember that this is a volunteer-driven project, and that contributions are welcome :)

Get Started!

Ready to contribute? Here’s how to set up CumulusCI for local development.

  1. Fork the CumulusCI repo on GitHub.

  2. Clone your fork to your local workspace.

  3. Create a fresh Python 3 virtual environment and activate it (to keep this isolated from other Python software on your machine). Here is one way:

    $ python3 -m venv cci_venv
    $ source cci_venv/bin/activate
  4. Install the development requirements:

    $ make dev-install
  5. Install pre-commit hooks for black and flake8:

    $ pre-commit install --install-hooks
  6. After making changes, run the tests and make sure they all pass:

    $ pytest
  7. Your new code should also have meaningful tests. One way to double check that your tests cover everything is to ensure that your new code has test code coverage:

    $ make coverage
  8. Push your changes to GitHub and submit a Pull Request. The base branch should be a new feature branch that we create to receive the changes (contact us to create the branch). This allows us to test the changes using our build system before merging to main.


We enable typeguard with pytest so if you add type declarations to your code, those declarations will be treated as runtime assertions in your Python tests.

Pull Request Guidelines

Before you submit a pull request, check that it meets these guidelines:

  • Documentation is updated to reflect all changes.

  • New classes, functions, etc have docstrings.

  • New code has comments.

  • Code style and file structure is similar to the rest of the project.

  • You have run the black code formatter.

Releasing CumulusCI

It’s easy to release a version of CumulusCI to GitHub and PyPI! First, create a new branch for your version:

$ git checkout -b feature/newversion

Make the necessary changes to prepare the new release:

  1. Update the version in cumulusci/version.txt

  2. Update the release notes in HISTORY.rst
    1. Navigate to the latest commits on the main branch here.

    2. Open all merge commits dating back to the previous release.

    3. Content under the “Critical Changes”, “Changes”, and “Issues Closed” headings of each of the pull request should be aggregated into the same sections under a new entry in the HISTORY.rst file.

Commit the changes, open a Pull Request on GitHub and request approval from another committer. Once your PR has been merged, a GitHub action will automatically create the release tag and push the artifacts to PyPI.

After a couple minutes, check for the new release’s appearance at PyPI.

Next, head to the tag that was autocreated in the GitHub repository and edit it. Populate the version number and paste in the changelog notes from HISTORY.rst. Note that some formatting, such as reStructuredText links, need to be converted to Markdown. Publish the release.


If pandoc is installed on macOS, you can run pbpaste | pandoc -f rst -t gfm | pbcopy to convert from RST to GitHub Flavored Markdown.

You can then create a pull request to update the Homebrew Tap by running this locally (note, it’s important to do this as soon as possible after the release is published on PyPI, because PyPI is the source CumulusCI checks to see if a new version is available):

$ git checkout main
$ git pull
$ make release-homebrew


The release-homebrew build step depends on the jq command line utility which is available via Homebrew.

That will create a new pull request in the SFDO-Tooling/homebrew-sfdo repository, which can be merged if its tests pass.

Finally, post the release notes to our usual channels:

Org-reliant Integration tests

Some tests are marked @pytest.mark.vcr() which means that they can either call into a real (configured) Salesforce org or use a cached YAML file of the request/response. To regenerate the VCR file, you can run pytest like this:

$ pytest cumulusci/.../test_<something>.py --org <orgname>

Where “orgname” is a configured org name like “qa”, “dev”, etc.

Periodically you can also do this, but it will take a LONG time:

$ pytest --org <orgname>

That will run all VCR-backed tests against the org, including all of the slow integration tests.

Some of these tests generate so much data or run so slowly that even the VCR tool does not help much. For example, if you are testing something that needs to download an entire org schema.

These tests can be marked with @pytest.mark.integration_test(). In that case, you can invoke them the same way as above, but you should not check in their YAML file into the repo. One of our files generates more than 300MB of cache data.

You can invoke these tests the same way:

$ pytest cumulusci/.../test_<something>.py --org qa

This will generate the cached data.

Later, you can use the cached data like this:

$ pytest cumulusci/.../test_<something>.py --accelerate-integration-tests

It will usually be much faster than calling into the Salesforce org, but it will still be quite slow compared to normal unit tests. Nevertheless, if you are changing feature tested by these tests, you should run them periodically.

Do not commit the files (“large_cassettes/*.yml”) to the repository.

Randomized tests

Tests should be executable in any order. You can run this command a few times to verify if they are:

pytest –random-order

It will output something like this:

Using –random-order-bucket=module Using –random-order-seed=986925

Using those two parameters on the command line, you can replicate a particular run later.