Unfurl is a command line tool for managing your DevOps infrastructure. Unfurl lets you easily track configuration, secrets, software and code dependencies, and deployment history all in git.
Unfurl can integrate with the DevOps tools you are already using – like Terraform, Ansible, and Helm – allowing you to encapsulate your DevOps processes into reusable building blocks and describe your cloud infrastructure in simple, application-centric terms.
The ultimate goal of Unfurl is enable anyone to clone, fork, and deploy live cloud services as easily as cloning and building code from git. So that we can have:
Fully-functional and reproducible archives of web applications.
Location independence to decentralize cloud infrastructure.
Cooperatively build and run cloud services the same way we build open source software.
How it works
unfurl init to create an Unfurl-managed git repository. Or use
unfurl clone to clone an existing one.
2. The repository will contain a few YAML files that you can edit. They will describe everything you’ll need to deploy your application, such as:
Cloud provider and SaaS services account credentials and other secrets organized into environments.
Code repositories and container image registries.
A high-level model of your cloud infrastructure and their dependencies such as compute instances and databases, described using the TOSCA standard.
Operations that invoke Terraform, Ansible, or other command-line tools (which Unfurl can automatically install).
unfurl deploy to deploy the infrastructure. Unfurl will generate a plan based on your target environment and high-level model and choose the correct operations to call. It will commit to git the latest configuration and a history of changes to your cloud accounts.
4. Now you have a reproducible description of your cloud infrastructure stored in git! So you can:
Push your repository to a git service such as Github or Gitlab to share it. For access control, each environment can be stored as separate git submodules or branches.
Pull incoming changes and review and approve pull requests before deploying.
Clone the repository and deploy to new environments even if they use different services – because is your model is adaptable, manual changes are minimized.
No server, agentless
Simple, stand-alone CLI that can be used both in your local development environment or in an automated CI/CD pipeline.
Deploy infrastructure from simple, application-centric descriptions
Model your cloud infrasture with OASIS TOSCA (Topology and Orchestration Specification for Cloud Applications) standard YAML vocabulary.
Import reusable and adaptable components or build (and publish) your own.
Easily declare dependencies to enable incremental deployment.
Path-based query DSL to express dynamic relationships between resources and configurations
Ansible-compatible Jinja2 templates provide an extensive, mature templating library.
Dynamic matching and substitution so models can adapt to different environments, services and cloud providers.
Integrates with the DevOps tools you are already using
Developer-friendly state management
All resource state is stored in git as human-readable and editable, application-centric resource state
You so can easily override or repair resource representations enabling interactive development.
Editor friendly config files:
Comments, order, and whitespace are preserved.
Syntactic macros for YAML provide generic facility for re-use and avoiding verbose, boiler-plate
Manage your local machine and bootstrap setup by deploying locally.
Downloads and installs specified software versions and code as deployment process.
Creates and manages isolated deployment environments
Isolated environments can be either a Docker container or a Python virtualenv with asdf.
Clear separation of local and shared configuration to avoid unnecessary local configuration steps.
Flexible Secrets Management
Declare secrets and protect your repository.
Inline encryption or use external secret managers like HashiCorp Vault.
Automatic encryption of files in
Sensitive content redacted in output and logs
“Day Two” Operations
Check, discover and repair commands
Define your own workflows for maintenance tasks like backup and restore.
unfurl is available on PyPI. You can install using
pip install unfurl
unfurl creates a virtual Python environment to run in so it only installs the minimal requirements needed to run the command line. If you want to run it using your system Python install it with the “full” option:
pip install unfurl[full]
You can also install
unfurl directly from this repository to get the latest code:
pip3 install -e "git+https://github.com/onecommons/unfurl.git#egg=unfurl"
Alternatively, you can use the Unfurl container on docker.io at
Linux or MacOs
Python (3.7, 3.8, 3.9 or 3.10); git
Python 3.6 is not tested automatically but should work. However you should make sure you have the latest version of pip installed (
pip install -U pip) and may need to have Rust installed for the crytography library.
Use the table below to activate shell autocompletion for the
Add this to
Add this to
Add this to
git clone --recurse-submodules https://github.com/onecommons/unfurl
To build documentation: Run
tox -e docs.
To build a distribution package run:
python setup.py sdist bdist_wheel
You can now install this package with pip, for example:
pip install ./dist/unfurl-0.2.2.dev3-py2.py3-none-any.whl
Running unit tests
You can use
tox to run the unit tests inside the supported python environments with the latest source installed.
pip install tox and then run
tox in source root. To install the dependencies you may need header files installed by the following OS packages:
openssl-dev. (Note: if installation of a dependency fails, reinvoke
-r to recreate the test environment.)
If you use
asdf to manage multiple versions of Python, also install
pip install tox-asdf.
-- are passed to the test runner, e.g. to run an individual test:
tox -- tests/test_runtime.py.
Status and Caveats
Unfurl is in early stages of development and should not be used in production. In particular be mindful of these limitations:
Only clone and deploy trusted repositories and projects. The docker runtime is not configured to provide isolation so you should assume any project may contain executable code that can gain full access to your system.
Locking to prevent multiple instances of Unfurl from modifying the same resources at the same time currently only works with instances accessing the same local copy of an ensemble.
Incremental updates are only partially implemented. You can incrementally update an ensemble by explicitly limiting jobs with the
--instancecommand line options.
Google Cloud SDK doesn’t yet work with Python 3.10.
Check out the rest of Unfurl’s documentation here