Configuration reference

This document is a reference for the CircleCI 2.x configuration keys that are used in the .circleci/config.yml file.

You can see a complete config.yml in our full example.

The version field is intended to be used in order to issue warnings for deprecation or breaking changes.

The setup field enables you to conditionally trigger configurations from outside the primary .circleci parent directory, update pipeline parameters, or generate customized configurations.

The following example uses the node orb that exists in the certified circleci namespace. Refer to the Node orb page in the Orb Registry for more examples and information.

version: 2.1

orbs:
  node: circleci/node@x.y

jobs:
  install-node-example:
    docker:
      - image: cimg/base:stable
    steps:
      - checkout
      - node/install:
          install-yarn: true
          node-version: '16.13'
      - run: node --version
workflows:
  test_my_app:
    jobs:
      - install-node-example

Documentation is available for Using Orbs and Authoring Orbs. Public orbs are listed in the Orb Registry.

A command defines a sequence of steps as a map to be executed in a job, enabling you to reuse a single command definition across multiple jobs. For more information see the Reusable Config Reference Guide.

Example:

commands:
  sayhello:
    description: "A very simple command for demonstration purposes"
    parameters:
      to:
        type: string
        default: "Hello World"
    steps:
      - run: echo << parameters.to >>

Pipeline parameters declared for use in the configuration. See Pipeline Values and Parameters for usage details.

Executors define the execution environment in which the steps of a job will be run, allowing you to reuse a single executor definition across multiple jobs.

⁽¹⁾ One executor type should be specified per job. If more than one is set you will receive an error.

Example:

version: 2.1
executors:
  my-executor:
    docker:
      - image: cimg/ruby:3.0.3-browsers
        auth:
          username: mydockerhub-user
          password: $DOCKERHUB_PASSWORD  # context / project UI env-var reference

jobs:
  my-job:
    executor: my-executor
    steps:
      - run: echo "Hello executor!"

See the Using Parameters in Executors section of the Reusing Config page for examples of parameterized executors.

A Workflow is comprised of one or more uniquely named jobs. Jobs are specified in the jobs map, see Sample config.yml for two examples of a job map. The name of the job is the key in the map, and the value is a map describing the job.

Each job consists of the job’s name as a key and a map as a value. A name should be case insensitive unique within a current jobs list. The value map has the following attributes:

⁽¹⁾ One executor type should be specified per job. If more than one is set you will receive an error.

A map of environment variable names and values. For more information on defining and using environment variables, and the order of precedence governing the various ways they can be set, see the Environment variables page.

If parallelism is set to N > 1, then N independent executors will be set up and each will run the steps of that job in parallel. This feature is used to optimize your test steps. Split your test suite, using the CircleCI CLI, across parallel containers so the job will complete in a shorter time. Certain parallelism-aware steps can opt out of the parallelism and only run on a single executor. Learn more on the Running Tests in Parallel page.

Example:

jobs:
  build:
    docker:
      - image: cimg/base:2022.09
        auth:
          username: mydockerhub-user
          password: $DOCKERHUB_PASSWORD  # context / project UI env-var reference
    environment:
      FOO: bar
    parallelism: 3
    resource_class: large
    working_directory: ~/my-app
    steps:
      - run: go test -v $(go list ./... | circleci tests split)

Job-level parameters can be used when calling a job in a workflow.

Reserved parameter-names:

• name
• requires
• context
• type
• filters
• matrix

See Parameter Syntax for definition details.

CircleCI offers several execution environments in which to run your jobs. To specify an execution environment choose an executor, then specify and image and a resource class. An executor defines the underlying technology, environment and operating system in which to run a job.

Set up your jobs to run using the docker (Linux), machine (LinuxVM, Windows, GPU, Arm), or macos executor, then specify an image with the tools and packages you need, and a resource class.

Learn more about execution environments and executors in the Introduction to Execution Environments.

Configured by docker key which takes a list of maps:

For a primary container (the first container in the list) if neither command nor entrypoint is specified in the config, then any ENTRYPOINT and COMMAND in the image are ignored. This is because the primary container is typically used only for running the steps and not for its ENTRYPOINT, and an ENTRYPOINT may consume significant resources or exit prematurely. A custom image may disable this behavior and force the ENTRYPOINT to run.

You can specify image versions using tags or digest. You can use any public images from any public Docker registry (defaults to Docker Hub). Learn more about specifying images on the Using the Docker Execution Environment page.

Some registries, Docker Hub, for example, may rate limit anonymous docker pulls. It is recommended you authenticate in such cases to pull private and public images. The username and password can be specified in the auth field. See Using Docker Authenticated Pulls for details.

Example:

jobs:
  build:
    docker:
      - image: buildpack-deps:trusty # primary container
        auth:
          username: mydockerhub-user
          password: $DOCKERHUB_PASSWORD  # context / project UI env-var reference
        environment:
          ENV: CI

      - image: mongo:2.6.8
        auth:
          username: mydockerhub-user
          password: $DOCKERHUB_PASSWORD  # context / project UI env-var reference
        command: [--smallfiles]

      - image: postgres:14.2
        auth:
          username: mydockerhub-user
          password: $DOCKERHUB_PASSWORD  # context / project UI env-var reference
        environment:
          POSTGRES_USER: user

      - image: redis@sha256:54057dd7e125ca41afe526a877e8bd35ec2cdd33b9217e022ed37bdcf7d09673
        auth:
          username: mydockerhub-user
          password: $DOCKERHUB_PASSWORD  # context / project UI env-var reference

      - image: acme-private/private-image:321
        auth:
          username: mydockerhub-user
          password: $DOCKERHUB_PASSWORD  # context / project UI env-var reference

Using an image hosted on AWS ECR requires authentication using AWS credentials. By default, CircleCI uses the AWS credentials you provide by setting the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY project environment variables. It is also possible to set the credentials by using the aws_auth field as in the following example:

jobs:
  build:
    docker:
      - image: account-id.dkr.ecr.us-east-1.amazonaws.com/org/repo:0.1
        aws_auth:
          aws_access_key_id: AKIAQWERVA  # can specify string literal values
          aws_secret_access_key: $ECR_AWS_SECRET_ACCESS_KEY  # or project UI envar reference

The machine executor is configured using the machine key, which takes a map:

Example:

version: 2.1
jobs:
  build:
    machine:
      image: ubuntu-2004:202010-01
    steps:
      - checkout
      - run:
          name: "Testing"
          command: echo "Hi"

Specifying an image in your config file is strongly recommended. CircleCI supports multiple Linux machine images that can be specified in the image field. For a full list of supported image tags, refer to the following pages in the Developer Hub:

• ubuntu-2004
• ubuntu-2204

More information on what software is available in each image can be found in our Discuss forum.

The machine executor supports Docker Layer Caching which is useful when you are building Docker images during your job or Workflow.

If you are using CircleCI server, contact your system administrator for details of available Linux machine images.

When using the Linux GPU executor, the available images are:

• ubuntu-2004-cuda-11.4:202110-01 - CUDA v11.4.2, Docker v20.10.7, nvidia-container-toolkit v1.5.1-1
• ubuntu-2004-cuda-11.2:202103-01 - CUDA v11.2.1, Docker v20.10.5, nvidia-container-toolkit v1.4.2-1
• ubuntu-1604-cuda-11.1:202012-01 - CUDA v11.1, Docker v19.03.13, nvidia-container-toolkit v1.4.0-1
• ubuntu-1604-cuda-10.2:202012-01 - CUDA v10.2, Docker v19.03.13, nvidia-container-toolkit v1.3.0-1
• ubuntu-1604-cuda-10.1:201909-23 - CUDA v10.1, Docker v19.03.0-ce, nvidia-docker v2.2.2
• ubuntu-1604-cuda-9.2:201909-23 - CUDA v9.2, Docker v19.03.0-ce, nvidia-docker v2.2.2

CircleCI supports running jobs on Android for testing and deploying Android applications.

To use the Android image directly with the machine executor, add the following to your job:

version: 2.1

jobs:
  build:
    machine:
      image: android:2022.09.1

The Android image can also be accessed using the Android orb.

For examples, refer to the Using Android Images with the Machine Executor page.

Specifying an image in your config file is strongly recommended. CircleCI supports multiple Windows machine images that can be specified in the image field.

For a full list of supported images, refer to one of the following:

• windows-server-2022-gui
• windows-server-2019

More information on what software is available in each image can be found in our Discuss forum.

Alternatively, use the Windows orb to manage your Windows execution environment. For examples, see the Using the Windows Execution Environment page.

If you are using CircleCI server, contact your system administrator for details of available Windows machine images.

When using the Windows GPU executor, the available image is:

• windows-server-2019-cuda

Example

version: 2.1

jobs:
  build:
    machine:
      image: windows-server-2019-cuda:current

CircleCI supports running jobs on macOS, to allow you to build, test, and deploy apps for macOS, iOS, tvOS and watchOS. To run a job in a macOS virtual machine, add the macos key to the top-level configuration for your job and specify the version of Xcode you would like to use.

Example: Use a macOS virtual machine with Xcode version 14.2.0:

jobs:
  build:
    macos:
      xcode: "14.2.0"

This key is deprecated. Use workflows filtering to control which jobs run for which branches.

The resource_class feature allows configuring CPU and RAM resources for each job. Resource classes are available for each execution environment, as described in the tables below.

We implement soft concurrency limits for each resource class to ensure our system remains stable for all customers. If you are on a Performance or custom plan and experience queuing for certain resource classes, it’s possible you are hitting these limits. Contact CircleCI support to request a raise on these limits for your account.

Use the resource_class key to configure a self-hosted runner instance.

For example:

jobs:
  job_name:
    machine: true
    resource_class: <my-namespace>/<my-runner>

Example:

jobs:
  build:
    docker:
      - image: cimg/base:2022.09
        auth:
          username: mydockerhub-user
          password: $DOCKERHUB_PASSWORD  # context / project UI env-var reference
    resource_class: xlarge
    steps:
      ... // other config

Example:

jobs:
  build:
    machine:
      image: ubuntu-2004:202010-01 # recommended linux image
    resource_class: large
    steps:
      ... // other config

jobs:
  build:
    machine: true
    resource_class: large
    steps:
      ... // other config

Example

jobs:
  build:
    macos:
      xcode: "14.2.0"
    resource_class: macos.x86.medium.gen2
    steps:
      ... // other config

If you are working on CircleCI server v3.1 and up, you can access the macOS execution environment using self-hosted runner.

Example:

version: 2.1

orbs:
  win: circleci/windows@4.1.1 # The Windows orb give you everything you need to start using the Windows executor.

jobs:
  build: # name of your job
    executor:
      name: win/default # executor type
      size: medium # can be medium, large, xlarge, 2xlarge

    steps:
      # Commands are run in a Windows virtual machine environment
      - checkout
      - run: Write-Host 'Hello, Windows'

jobs:
  build: # name of your job
    machine:
      image: 'windows-server-2022-gui:current'
      shell: 'powershell.exe -ExecutionPolicy Bypass'
    resource_class: windows.medium # can be medium, large, xlarge, 2xlarge
    steps:
      # Commands are run in a Windows virtual machine environment
        - checkout
        - run: Write-Host 'Hello, Windows'

jobs:
  build: # name of your job
    machine:
      image: windows-default
    resource_class: windows.medium # can be medium, large, xlarge, 2xlarge
    steps:
      # Commands are run in a Windows virtual machine environment
        - checkout
        - run: Write-Host 'Hello, Windows'

Note: These resources require review by our support team. Open a support ticket if you would like to request access.

Example:

version: 2.1

jobs:
  build:
    machine:
      image: ubuntu-1604-cuda-10.1:201909-23
    resource_class: gpu.nvidia.small
    steps:
      - run: nvidia-smi
      - run: docker run --gpus all nvidia/cuda:9.0-base nvidia-smi

See the Available Linux GPU images section for the full list of available images.

Note: These resources require review by our support team. Open a support ticket if you would like to request access.

Example:

version: 2.1
orbs:
  win: circleci/windows@5.0.0

jobs:
  build:
    executor: win/server-2019-cuda
    steps:
      - checkout
      - run: '&"C:\Program Files\NVIDIA Corporation\NVSMI\nvidia-smi.exe"'

⁽²⁾ This resource requires review by our support team. Open a support ticket if you would like to request access.

Example:

jobs:
  my-job:
    machine:
      image: ubuntu-2004:202101-01
    resource_class: arm.medium
    steps:
      - run: uname -a
      - run: echo "Hello, Arm!"

jobs:
  my-job:
    machine:
      image: arm-default
    resource_class: arm.medium
    steps:
      - run: uname -a
      - run: echo "Hello, Arm!"

The steps setting in a job should be a list of single key/value pairs, the key of which indicates the step type. The value may be either a configuration map or a string (depending on what that type of step requires). For example, using a map:

jobs:
  build:
    working_directory: ~/canary-python
    environment:
      FOO: bar
    steps:
      - run:
          name: Running tests
          command: make test

Here run is a step type. The name attribute is used by the UI for display purposes. The command attribute is specific for run step and defines command to execute.

Some steps may implement a shorthand semantic. For example, run may be also be called like this:

jobs:
  build:
    steps:
      - run: make test

In its short form, the run step allows us to directly specify which command to execute as a string value. In this case step itself provides default suitable values for other attributes (name here will have the same value as command, for example).

Another shorthand, which is possible for some steps, is to simply use the step name as a string instead of a key/value pair:

jobs:
  build:
    steps:
      - checkout

In this case, the checkout step will checkout project source code into the job’s working_directory.

In general all steps can be described as:

Each built-in step is described in detail below.

Used for invoking all command-line programs, taking either a map of configuration values, or, when called in its short-form, a string that will be used as both the command and name. Run commands are executed using non-login shells by default, so you must explicitly source any dotfiles as part of the command.

Each run declaration represents a new shell. It is possible to specify a multi-line command, each line of which will be run in the same shell:

- run:
    command: |
      echo Running test
      mkdir -p /tmp/test-results
      make test

You can also configure commands to run in the background if you don’t want to wait for the step to complete before moving on to subsequent run steps.

For jobs that run on Linux, the default value of the shell option is /bin/bash -eo pipefail if /bin/bash is present in the build container. Otherwise it is /bin/sh -eo pipefail. The default shell is not a login shell (--login or -l are not specified). Hence, the shell will not source your ~/.bash_profile, ~/.bash_login, ~/.profile files.

For jobs that run on macOS, the default shell is /bin/bash --login -eo pipefail. The shell is a non-interactive login shell. The shell will execute /etc/profile/ followed by ~/.bash_profile before every step.

For more information about which files are executed when bash is invocated, see the INVOCATION section of the bash manpage.

Descriptions of the -eo pipefail options are provided below.

-e

Exit immediately if a pipeline (which may consist of a single simple command), a subshell command enclosed in parentheses, or one of the commands executed as part of a command list enclosed by braces exits with a non-zero status.

So if in the previous example mkdir failed to create a directory and returned a non-zero status, then command execution would be terminated, and the whole step would be marked as failed. If you desire the opposite behaviour, you need to add set +e in your command or override the default shell in your configuration map of run. For example:

- run:
    command: |
      echo Running test
      set +e
      mkdir -p /tmp/test-results
      make test

- run:
    shell: /bin/sh
    command: |
      echo Running test
      mkdir -p /tmp/test-results
      make test

-o pipefail

If pipefail is enabled, the pipeline’s return status is the value of the last (rightmost) command to exit with a non-zero status, or zero if all commands exit successfully. The shell waits for all commands in the pipeline to terminate before returning a value.

For example:

- run: make test | tee test-output.log

If make test fails, the -o pipefail option will cause the whole step to fail. Without -o pipefail, the step will always run successfully because the result of the whole pipeline is determined by the last command (tee test-output.log), which will always return a zero status.

Note that even if make test fails the rest of pipeline will be executed.

If you want to avoid this behaviour, you can specify set +o pipefail in the command or override the whole shell (see example above).

In general, we recommend using the default options (-eo pipefail) because they show errors in intermediate commands and simplify debugging job failures. For convenience, the UI displays the used shell and all active options for each run step.

For more information, see the Using Shell Scripts document.

The background attribute enables you to configure commands to run in the background. Job execution will immediately proceed to the next step rather than waiting for return of a command with the background attribute set to true. The following example shows the config for running the X virtual framebuffer in the background which is commonly required to run Selenium tests:

- run:
    name: Running X virtual framebuffer
    command: Xvfb :99 -screen 0 1280x1024x24
    background: true

- run: make test

run has a very convenient shorthand syntax:

- run: make test

# shorthanded command can also have multiple lines
- run: |
    mkdir -p /tmp/test-results
    make test

In this case, command and name become the string value of run, and the rest of the config map for that run have their default values.

By default, CircleCI will execute job steps one at a time, in the order that they are defined in config.yml, until a step fails (returns a non-zero exit code). After a command fails, no further job steps will be executed.

Adding the when attribute to a job step allows you to override this default behaviour, and selectively run or skip steps depending on the status of the job.

The default value of on_success means that the step will run only if all of the previous steps have been successful (returned exit code 0).

A value of always means that the step will run regardless of the exit status of previous steps. This is useful if you have a task that you want to run regardless of whether the previous steps are successful or not. For example, you might have a job step that needs to upload logs or code-coverage data somewhere.

A value of on_fail means that the step will run only if one of the preceding steps has failed (returns a non-zero exit code). It is common to use on_fail if you want to store some diagnostic data to help debug test failures, or to run custom notifications about the failure, such as sending emails or triggering alerts in chatrooms.

- run:
    name: Upload CodeCov.io Data
    command: bash <(curl -s https://codecov.io/bash) -F unittests
    when: always # Uploads code coverage results, pass or fail

A job can exit without failing by using run: circleci-agent step halt. However, if a step within the job is already failing then the job will continue to fail. This can be useful in situations where jobs need to conditionally execute.

Here is an example where halt is used to avoid running a job on the develop branch:

run: |
    if [ "$CIRCLE_BRANCH" = "develop" ]; then
        circleci-agent step halt
    fi

A conditional step consists of a step with the key when or unless. Under the when key are the subkeys condition and steps. The purpose of the when step is customizing commands and job configuration to run on custom conditions (determined at config-compile time) that are checked before a workflow runs. See the Conditional Steps section of the Reusing Config document for more details.

Example:

version: 2.1

jobs: # conditional steps may also be defined in `commands:`
  job_with_optional_custom_checkout:
    parameters:
      custom_checkout:
        type: string
        default: ""
    machine:
      image: ubuntu-2004:202107-02
    steps:
      - when:
          condition: <<parameters.custom_checkout>>
          steps:
            - run: echo "my custom checkout"
      - unless:
          condition: <<parameters.custom_checkout>>
          steps:
            - checkout
workflows:
  build-test-deploy:
    jobs:
      - job_with_optional_custom_checkout:
          custom_checkout: "any non-empty string is truthy"
      - job_with_optional_custom_checkout

A special step used to check out source code to the configured path (defaults to the working_directory). The reason this is a special step is because it is more of a helper function designed to make checking out code easy for you. If you require doing git over HTTPS you should not use this step as it configures git to checkout over ssh.

If path already exists and is:

• a git repo - step will not clone whole repo, instead will fetch origin
• NOT a git repo - step will fail.

In the case of checkout, the step type is just a string with no additional attributes:

- checkout

The checkout command automatically adds the required authenticity keys for interacting with GitHub and Bitbucket over SSH, which is detailed further in our integration guide – this guide will also be helpful if you wish to implement a custom checkout command.

CircleCI does not check out submodules. If your project requires submodules, add run steps with appropriate commands as shown in the following example:

- checkout
- run: git submodule sync
- run: git submodule update --init

Allows Docker commands to be run locally. See Running Docker Commands for details.

jobs:
  build:
    docker:
      - image: cimg/base:2022.06
    steps:
      # ... steps for building/testing app ...
      - setup_remote_docker:
          version: 20.10.14

Notes:

• setup_remote_docker is not compatible with the machine executor. See Docker Layer Caching in Machine Executor for information on how to enable DLC with the machine executor.
• The version key is not currently supported on CircleCI server. Contact your system administrator for information about the Docker version installed in your remote Docker environment.

Generates and stores a cache of a file or directory of files such as dependencies or source code in our object storage. Later jobs can restore this cache. Learn more on the Caching Dependencies page.

Cache retention can be customized on the CircleCI web app by navigating to Plan > Usage Controls.

The cache for a specific key is immutable and cannot be changed once written.

Note: If the cache for the given key already exists it will not be modified, and job execution will proceed to the next step.

When storing a new cache, the key value may contain special templated values for your convenience:

During step execution, the templates above will be replaced by runtime values and use the resultant string as the key.

Template examples:

• myapp-{{ checksum "package-lock.json" }} - cache will be regenerated every time something is changed in package-lock.json file, different branches of this project will generate the same cache key.
• myapp-{{ .Branch }}-{{ checksum "package-lock.json" }} - same as the previous one, but each branch will generate separate cache
• myapp-{{ epoch }} - every run of a job will generate a separate cache

While choosing suitable templates for your cache key, keep in mind that cache saving is not a free operation, because it will take some time to upload the cache to our storage. So it makes sense to have a key that generates a new cache only if something actually changed and avoid generating a new one every run of a job.

Example:

- save_cache:
    key: v1-myapp-{{ arch }}-{{ checksum "project.clj" }}
    paths:
      - /home/ubuntu/.m2

Notes:

• Wildcards are not currently supported in save_cache paths. Please visit the Ideas board and vote for this feature if it would be useful for you or your organization.

• In some instances, a workaround for this is to save a particular workspace to cache:

- save_cache:
    key: v1-{{ checksum "yarn.lock" }}
    paths:
      - node_modules/workspace-a
      - node_modules/workspace-c

Restores a previously saved cache based on a key. Cache needs to have been saved first for this key using the save_cache step. Learn more in the caching documentation.

⁽¹⁾ at least one attribute has to be present. If key and keys are both given, key will be checked first, and then keys.

A key is searched against existing keys as a prefix.

Note: When there are multiple matches, the most recent match will be used, even if there is a more precise match.

For example:

steps:
  - save_cache:
      key: v1-myapp-cache
      paths:
        - ~/d1

  - save_cache:
      key: v1-myapp-cache-new
      paths:
        - ~/d2

  - run: rm -f ~/d1 ~/d2

  - restore_cache:
      key: v1-myapp-cache

In this case cache v1-myapp-cache-new will be restored because it’s the most recent match with v1-myapp-cache prefix even if the first key (v1-myapp-cache) has exact match.

For more information on key formatting, see the key section of save_cache step.

When CircleCI encounters a list of keys, the cache will be restored from the first one matching an existing cache. Most probably you would want to have a more specific key to be first (for example, cache for exact version of package-lock.json file) and more generic keys after (for example, any cache for this project). If no key has a cache that exists, the step will be skipped with a warning.

A path is not required here because the cache will be restored to the location from which it was originally saved.

Example:

- restore_cache:
    keys:
      - v1-myapp-{{ arch }}-{{ checksum "project.clj" }}
      # if cache for exact version of `project.clj` is not present then load any most recent one
      - v1-myapp-

# ... Steps building and testing your application ...

# cache will be saved only once for each version of `project.clj`
- save_cache:
    key: v1-myapp-{{ arch }}-{{ checksum "project.clj" }}
    paths:
      - /foo

See run for current processes. If you have parallelism > 1 in your job, see the Migrate from deploy to run guide.

Step to store artifacts (for example logs, binaries, etc) to be available in the web app or through the API. See the Uploading Artifacts document for more information.

There can be multiple store_artifacts steps in a job. Using a unique prefix for each step prevents them from overwriting files.

Artifact storage retention can be customized on the CircleCI web app by navigating to Plan > Usage Controls.

Example:

- run:
    name: Build the Jekyll site
    command: bundle exec jekyll build --source jekyll --destination jekyll/_site/docs/
- store_artifacts:
    path: jekyll/_site/docs/
    destination: circleci-docs

Special step used to upload and store test results for a build. Test results are visible on the CircleCI web application under each build’s Test Summary section. Storing test results is useful for timing analysis of your test suites. For more information on storing test results, see the Collecting Test Data page.

It is also possible to store test results as a build artifact; to do so, please refer to the store_artifacts step.

Example:

Directory structure:

test-results
├── jest
│   └── results.xml
├── mocha
│   └── results.xml
└── rspec
    └── results.xml

config.yml syntax:

- store_test_results:
    path: test-results

Special step used to persist a temporary file to be used by another job in the workflow. For more information on using workspaces, see the Using Workspaces to Share Data Between Jobs page.

persist_to_workspace adopts the storage settings from the storage customization controls on the CircleCI web app. If no custom setting is provided, persist_to_workspace defaults to 15 days.

Workspace storage retention can be customized on the CircleCI web app by navigating to Plan > Usage Controls.

The root key is a directory on the container which is taken to be the root directory of the workspace. The paths values are all relative to the root.

Example for root Key

For example, the following step syntax persists the specified paths from /tmp/dir into the workspace, relative to the directory /tmp/dir.

- persist_to_workspace:
    root: /tmp/dir
    paths:
      - foo/bar
      - baz

After this step completes, the following directories are added to the workspace:

/tmp/dir/foo/bar
/tmp/dir/baz

Example for paths Key

- persist_to_workspace:
    root: /tmp/workspace
    paths:
      - target/application.jar
      - build/*

The paths list uses Glob from Go, and the pattern matches filepath.Match.

pattern:
        { term }
term:
        '*' matches any sequence of non-Separator characters
        '?' matches any single non-Separator character
        '[' [ '^' ] { character-range }
        ']' character class (must be non-empty)
        c matches character c (c != '*', '?', '\\', '[')
        '\\' c matches character c
character-range:
        c matches character c (c != '\\', '-', ']')
        '\\' c matches character c
        lo '-' hi matches character c for lo <= c <= hi

The Go documentation states that the pattern may describe hierarchical names such as /usr/*/bin/ed (assuming the Separator is ‘/’). Note: Everything must be relative to the work space root directory.

Special step used to attach the workflow’s workspace to the current container. The full contents of the workspace are downloaded and copied into the directory the workspace is being attached at. For more information on using workspaces, see the Using Workspaces to Share Data Between Jobs page.

Workspace storage retention can be customized on the CircleCI web app by navigating to Plan > Usage Controls.

Example:

- attach_workspace:
    at: /tmp/workspace

Special step that adds SSH keys from a project’s settings to a container. Also configures SSH to use these keys. For more information on SSH keys see the Create additional GitHub SSH keys page.

steps:
  - add_ssh_keys:
      fingerprints:
        - "b7:35:a6:4e:9b:0d:6d:d4:78:1e:9a:97:2a:66:6b:be"

Note: Even though CircleCI uses ssh-agent to sign all added SSH keys, you must use the add_ssh_keys key to actually add keys to a container.

Pipeline values are available to all pipeline configurations and can be used without previous declaration. The pipeline values available are as follows:

For a list of pipeline values compatible with GitLab, see the Pipeline values and parameters page.

For example:

version: 2.1
jobs:
  build:
    docker:
      - image: cimg/node:17.2.0
        auth:
          username: mydockerhub-user
          password: $DOCKERHUB_PASSWORD  # context / project UI env-var reference
    environment:
      IMAGETAG: latest
    working_directory: ~/main
    steps:
      - run: echo "This is pipeline ID << pipeline.id >>"

Enables jobs to go through a set of well-defined IP address ranges. See IP ranges for details.

Example:

version: 2.1

jobs:
  build:
    circleci_ip_ranges: true # opts the job into the IP ranges feature
    docker:
      - image: curlimages/curl
        auth:
          username: mydockerhub-user
          password: $DOCKERHUB_PASSWORD  # context / project UI env-var reference
    steps:
      - run: echo “Hello World”
workflows:
  build-workflow:
    jobs:
      - build

Notes:

• A paid account on a Performance or Scale plan is required to access IP ranges.

Used for orchestrating all jobs. Each workflow consists of the workflow name as a key and a map as a value. A name should be unique within the current config.yml. The top-level keys for the Workflows configuration are version and jobs. For more information, see the Using Workflows to Orchestrate Jobs page.

The Workflows version field is used to issue warnings for deprecation or breaking changes.

A unique name for your workflow.

Specifies which triggers will cause this workflow to be executed. Default behavior is to trigger the workflow when pushing to a branch.

A workflow may have a schedule indicating it runs at a certain time, for example a nightly build that runs every day at 12am UTC:

workflows:
   nightly:
     triggers:
       - schedule:
           cron: "0 0 * * *"
           filters:
             branches:
               only:
                 - main
                 - beta
     jobs:
       - test

The cron key is defined using POSIX crontab syntax.

Trigger Filters can have the key branches.

The branches key controls whether the current branch should have a schedule trigger created for it, where current branch is the branch containing the config.yml file with the trigger stanza. That is, a push on the main branch will only schedule a workflow for the main branch.

Branches can have the keys only and ignore which each map to a single string naming a branch. You may also use regular expressions to match against branches by enclosing them with /’s, or map to a list of such strings. Regular expressions must match the entire string.

• Any branches that match only will run the job.
• Any branches that match ignore will not run the job.
• If neither only nor ignore are specified then all branches will run the job. If both only and ignore are specified, the only is used and ignore will have no effect.

You may use a when clause (the inverse clause unless is also supported) under a workflow declaration with a logic statement to determine whether or not to run that workflow.

The example configuration below uses a pipeline parameter, run_integration_tests to drive the integration_tests workflow.

version: 2.1

parameters:
  run_integration_tests:
    type: boolean
    default: false

workflows:
  integration_tests:
    when: << pipeline.parameters.run_integration_tests >>
    jobs:
      - mytestjob

jobs:
...

This example prevents the workflow integration_tests from running unless the tests are invoked explicitly when the pipeline is triggered with the following in the POST body:

{
    "parameters": {
        "run_integration_tests": true
    }
}

Refer to the Workflows for more examples and conceptual information.

A job can have the keys requires, name, context, type, and filters.

A job name that exists in your config.yml.

Jobs are run in parallel by default, so you must explicitly require any dependencies by their job name.

The name key can be used to invoke reusable jobs across any number of workflows. Using the name key ensures numbers are not appended to your job name (i.e. sayhello-1 , sayhello-2, etc.). The name you assign to the name key needs to be unique, otherwise the numbers will still be appended to the job name.

Jobs may be configured to use global environment variables set for an organization, see the Contexts document for adding a context in the application settings.

A job may have a type of approval indicating it must be manually approved before downstream jobs may proceed. For more information see the Using Workflows to Orchestrate Jobs page.

Jobs run in the dependency order until the workflow processes a job with the type: approval key followed by a job on which it depends, for example:

      - hold:
          type: approval
          requires:
            - test1
            - test2
      - deploy:
          requires:
            - hold

Job Filters can have the key branches or tags.

The following is an example of how the CircleCI documentation uses a regex to filter running a workflow for building PDF documentation:

# ...
workflows:
  build-deploy:
    jobs:
      - js_build
      - build_server_pdfs: # << the job to conditionally run based on the filter-by-branch-name.
          filters:
            branches:
              only: /server\/.*/

The above snippet causes the job build_server_pdfs to only be run when the branch being built starts with “server/”.

You can read more about using regex in your config in the Using Workflows to Schedule Jobs page.

Branches can have the keys only and ignore which either map to a single string naming a branch. You may also use regular expressions to match against branches by enclosing them with slashes, or map to a list of such strings. Regular expressions must match the entire string.

• Any branches that match only will run the job.
• Any branches that match ignore will not run the job.
• If neither only nor ignore are specified then all branches will run the job.
• If both only and ignore are specified the only is considered before ignore.

CircleCI does not run workflows for tags unless you explicitly specify tag filters. Additionally, if a job requires any other jobs (directly or indirectly), you must specify tag filters for those jobs.

Tags can have the keys only and ignore. You may also use regular expressions to match against tags by enclosing them with slashes, or map to a list of such strings. Regular expressions must match the entire string. Both lightweight and annotated tags are supported.

• Any tags that match only will run the job.
• Any tags that match ignore will not run the job.
• If neither only nor ignore are specified then the job is skipped for all tags.
• If both only and ignore are specified the only is considered before ignore.

For more information, see the Executing Workflows For a Git Tag section of the Workflows document.

The matrix stanza allows you to run a parameterized job multiple times with different arguments. For more information see the how-to guide on Using Matrix Jobs. In order to use the matrix stanza, you must use parameterized jobs.

Example:

The following is a basic example of using matrix jobs.

workflows:
  workflow:
    jobs:
      - build:
          matrix:
            parameters:
              version: ["0.1", "0.2", "0.3"]
              platform: ["macos", "windows", "linux"]

This expands to 9 different build jobs, and could be equivalently written as:

workflows:
  workflow:
    jobs:
      - build:
          name: build-macos-0.1
          version: 0.1
          platform: macos
      - build:
          name: build-macos-0.2
          version: 0.2
          platform: macos
      - build:
          name: build-macos-0.3
          version: 0.3
          platform: macos
      - build:
          name: build-windows-0.1
          version: 0.1
          platform: windows
      - ...

Sometimes you may wish to run a job with every combination of arguments except some value or values. You can use an exclude stanza to achieve this:

workflows:
  workflow:
    jobs:
      - build:
          matrix:
            parameters:
              a: [1, 2, 3]
              b: [4, 5, 6]
            exclude:
              - a: 3
                b: 5

The matrix above would expand into 8 jobs: every combination of the parameters a and b, excluding {a: 3, b: 5}

To require an entire matrix (every job within the matrix), use its alias. The alias defaults to the name of the job being invoked.

workflows:
  workflow:
    jobs:
      - deploy:
          matrix:
            parameters:
              version: ["0.1", "0.2"]
      - another-job:
          requires:
            - deploy

This means that another-job will require both deploy jobs in the matrix to finish before it runs.

Additionally, matrix jobs expose their parameter values via << matrix.* >> which can be used to generate more complex workflows. For example, here is a deploy matrix where each job waits for its respective build job in another matrix.

workflows:
  workflow:
    jobs:
      - build:
          name: build-v<< matrix.version >>
          matrix:
            parameters:
              version: ["0.1", "0.2"]
      - deploy:
          name: deploy-v<< matrix.version >>
          matrix:
            parameters:
              version: ["0.1", "0.2"]
          requires:
            - build-v<< matrix.version >>

This workflow will expand to:

workflows:
  workflow:
    jobs:
      - build:
          name: build-v0.1
          version: 0.1
      - build:
          name: build-v0.2
          version: 0.2
      - deploy:
          name: deploy-v0.1
          version: 0.1
          requires:
            - build-v0.1
      - deploy:
          name: deploy-v0.2
          version: 0.2
          requires:
            - build-v0.2

Every job invocation in a workflow may optionally accept two special arguments: pre-steps and post-steps.

Steps under pre-steps are executed before any of the other steps in the job. The steps under post-steps are executed after all of the other steps.

Pre and post steps allow you to execute steps in a given job without modifying the job. This is useful, for example, to run custom setup steps before job execution.

version: 2.1

jobs:
  bar:
    machine:
      image: ubuntu-2004:202107-02
    steps:
      - checkout
      - run:
          command: echo "building"
      - run:
          command: echo "testing"

workflows:
  build:
    jobs:
      - bar:
          pre-steps: # steps to run before steps defined in the job bar
            - run:
                command: echo "install custom dependency"
          post-steps: # steps to run after steps defined in the job bar
            - run:
                command: echo "upload artifact to s3"

Certain dynamic configuration features accept logic statements as arguments. Logic statements are evaluated to boolean values at configuration compilation time, that is, before the workflow is run. The group of logic statements includes:

The following logic values are considered falsy:

• false
• null
• 0
• NaN
• empty strings (“”)
• statements with no arguments

All other values are truthy. Also note that using logic with an empty list will cause a validation error.

Logic statements always evaluate to a boolean value at the top level, and coerce as necessary. They can be nested in an arbitrary fashion, according to their argument specifications, and to a maximum depth of 100 levels.

matches uses Java regular expressions for its pattern. A full match pattern must be provided, prefix matching is not an option. Though, it is recommended to enclose a pattern in ^ and $ to avoid accidental partial matches.

Note: When using logic statements at the workflow level, do not include the condition: key (the condition key is only needed for job level logic statements).

workflows:
  my-workflow:
    when:
      or:
        - equal: [ main, << pipeline.git.branch >> ]
        - equal: [ staging, << pipeline.git.branch >> ]

workflows:
  my-workflow:
    when:
      and:
        - not:
            matches:
              pattern: "^main$"
              value: << pipeline.git.branch >>
        - or:
            - equal: [ canary, << pipeline.git.tag >> ]
            - << pipeline.parameters.deploy-canary >>

version: 2.1

executors:
  linux-13:
    docker:
      - image: cimg/node:13.13
        auth:
          username: mydockerhub-user
          password: $DOCKERHUB_PASSWORD  # context / project UI env-var reference
  macos: &macos-executor
    macos:
      xcode: 14.2.0

jobs:
  test:
    parameters:
      os:
        type: executor
      node-version:
        type: string
    executor: << parameters.os >>
    steps:
      - checkout
      - when:
          condition:
            equal: [ *macos-executor, << parameters.os >> ]
          steps:
            - run: echo << parameters.node-version >>
      - run: echo 0

workflows:
  all-tests:
    jobs:
      - test:
          os: macos
          node-version: "13.13.0"

version: 2.1
jobs:
  build:
    docker:
      - image: ubuntu:14.04
        auth:
          username: mydockerhub-user
          password: $DOCKERHUB_PASSWORD  # context / project UI env-var reference

      - image: mongo:2.6.8
        auth:
          username: mydockerhub-user
          password: $DOCKERHUB_PASSWORD  # context / project UI env-var reference
        command: [mongod, --smallfiles]

      - image: postgres:14.2
        auth:
          username: mydockerhub-user
          password: $DOCKERHUB_PASSWORD  # context / project UI env-var reference
        # some containers require setting environment variables
        environment:
          POSTGRES_USER: user

      - image: redis@sha256:54057dd7e125ca41afe526a877e8bd35ec2cdd33b9217e022ed37bdcf7d09673
        auth:
          username: mydockerhub-user
          password: $DOCKERHUB_PASSWORD  # context / project UI env-var reference

      - image: rabbitmq:3.5.4
        auth:
          username: mydockerhub-user
          password: $DOCKERHUB_PASSWORD  # context / project UI env-var reference

    environment:
      TEST_REPORTS: /tmp/test-reports

    working_directory: ~/my-project

    steps:
      - checkout

      - run:
          command: echo 127.0.0.1 devhost | sudo tee -a /etc/hosts

      # Create Postgres users and database
      # Note the YAML heredoc '|' for nicer formatting
      - run: |
          sudo -u root createuser -h localhost --superuser ubuntu &&
          sudo createdb -h localhost test_db

      - restore_cache:
          keys:
            - v1-my-project-{{ checksum "project.clj" }}
            - v1-my-project-

      - run:
          environment:
            SSH_TARGET: "localhost"
            TEST_ENV: "linux"
          command: |
            set -xu
            mkdir -p ${TEST_REPORTS}
            run-tests.sh
            cp out/tests/*.xml ${TEST_REPORTS}

      - run: |
          set -xu
          mkdir -p /tmp/artifacts
          create_jars.sh << pipeline.number >>
          cp *.jar /tmp/artifacts

      - save_cache:
          key: v1-my-project-{{ checksum "project.clj" }}
          paths:
            - ~/.m2

      # Save artifacts
      - store_artifacts:
          path: /tmp/artifacts
          destination: build

      # Upload test results
      - store_test_results:
          path: /tmp/test-reports

  deploy-stage:
    docker:
      - image: ubuntu:14.04
        auth:
          username: mydockerhub-user
          password: $DOCKERHUB_PASSWORD  # context / project UI env-var reference
    working_directory: /tmp/my-project
    steps:
      - run:
          name: Deploy if tests pass and branch is Staging
          command: ansible-playbook site.yml -i staging

  deploy-prod:
    docker:
      - image: ubuntu:14.04
        auth:
          username: mydockerhub-user
          password: $DOCKERHUB_PASSWORD  # context / project UI env-var reference
    working_directory: /tmp/my-project
    steps:
      - run:
          name: Deploy if tests pass and branch is Main
          command: ansible-playbook site.yml -i production

workflows:
  build-deploy:
    jobs:
      - build:
          filters:
            branches:
              ignore:
                - develop
                - /feature-.*/
      - deploy-stage:
          requires:
            - build
          filters:
            branches:
              only: staging
      - deploy-prod:
          requires:
            - build
          filters:
            branches:
              only: main