Getting Started

Git Branches

The Nautobot project follows a branching model based on Git-flow. As such, there are two persistent git branches:

  • main - Serves as a snapshot of the current stable release
  • develop - All development on the upcoming stable release occurs here

At any given time, there may additionally be zero or more long-lived branches of the form develop-X.Y.Z, where X.Y.Z is a future stable release later than the one currently being worked on in the main develop branch.

You will always base pull requests off of the develop branch, or off of develop-X.Y.Z if you're working on a feature targeted for a later release. Never target pull requests into the main branch, which receives merges only from the develop branch.

Forking the Repo

When developing Nautobot, you'll be working on your own fork, so your first step will be to fork the official GitHub repository. You will then clone your GitHub fork locally for development.


It is highly recommended that you use SSH with GitHub. If you haven't already, make sure that you setup Git and add an SSH key to your GitHub account before proceeding.

In this guide, SSH will be used to interact with Git.

$ git clone
Cloning into 'nautobot'...
remote: Enumerating objects: 231, done.
remote: Counting objects: 100% (231/231), done.
remote: Compressing objects: 100% (147/147), done.
remote: Total 56705 (delta 134), reused 145 (delta 84), pack-reused 56474
Receiving objects: 100% (56705/56705), 27.96 MiB | 34.92 MiB/s, done.
Resolving deltas: 100% (44177/44177), done.
$ ls nautobot/    docs        nautobot.code-workspace  site  contrib   poetry.lock    
LICENSE.txt      development  mkdocs.yml  pyproject.toml 
NOTICE           dist         nautobot    scripts

About Remote Repos

Git refers to remote repositories as remotes. When you make your initial clone of your fork, Git defaults to naming this remote origin. Throughout this documentation, the following remote names will be used:

  • origin - The default remote name used to refer to your fork of Nautobot
  • upstream - The main remote used to refer to the official Nautobot repository

Setting up your Remotes

Remote repos are managed using the git remote command.

Upon cloning Nautobot for the first time, you will have only a single remote:

$ git remote -v
origin (fetch)
origin (push)

Add the official Nautobot repo as a the upstream remote:

$ git remote add upstream

View your remotes again to confirm you've got both origin pointing to your fork and upstream pointing to the official repo:

$ git remote -v
origin (fetch)
origin (push)
upstream (fetch)
upstream (push)

You're now ready to proceed to the next steps.


You will always push changes to origin (your fork) and pull changes from upstream (official repo).

Creating a Branch

Before you make any changes, always create a new branch. In the majority of cases, you'll always want to create your branches from the develop branch.

Before you ever create a new branch, always checkout the develop branch and make sure you you've got the latest changes from upstream.

$ git checkout develop
$ git pull upstream develop


If you do not do this, you run the risk of having merge conflicts in your branch, and that's never fun to deal with. Trust us on this one.

Now that you've got the latest upstream changes, create your branch. It's convention to always prefix your branch name with your GitHub username, separated by hyphens. For example:

$ git checkout -b yourusername-myfeature

Enabling Pre-Commit Hooks

Nautobot ships with a Git pre-commit hook script that automatically checks for style compliance and missing database migrations prior to committing changes. This helps avoid erroneous commits that result in CI test failures.


This pre-commit hook currently only supports the Python Virtual Environment Workflow.

You are encouraged to enable it by creating a link to scripts/git-hooks/pre-commit:

$ cd .git/hooks/
$ ln -s ../../scripts/git-hooks/pre-commit

Setting up your Development Environment

Getting started with Nautobot development is pretty straightforward, and should feel very familiar to anyone with Django development experience. We can recommend either a Docker Compose workflow (if you don't want to install dependencies such as PostgreSQL and Redis directly onto your system) or a Python virtual environment workflow.

Docker Compose Workflow

This workflow uses Docker and Docker Compose and assumes that you have them installed.

For the Docker Compose workflow, Nautobot uses Invoke as a replacement for Make. Invoke was chosen because it is less arcane than make. Instead of a Makefile, Invoke reads the in the project root.


Although the Docker Compose workflow uses containers, it is important to note that the containers are running the local repository code on your machine. Changes you make to your local code will be picked up and executed by the containers.

Install Invoke

Because it is used to execute all common Docker workflow tasks, Invoke must be installed for your user environment. On most systems, if you're installing without root/superuser permissions, the default will install into your local user environment.

$ pip3 install invoke

If you run into issues, you may also deliberately tell pip3 to install into your user environment by adding the --user flag:

$ pip3 install --user invoke

Please see the official documentation on Pip user installs for more information.

List Invoke Tasks

Now that you have an invoke command, list the tasks defined in

$ invoke --list
Available tasks:

  black               Check Python code style with Black.
  build               Build all docker images.
  cli                 Launch a bash shell inside the running Nautobot container.
  createsuperuser     Create a new Nautobot superuser account (default: "admin"), will prompt for password.
  dumpdata            Dump database data into file, only for development environment use.
  debug               Start Nautobot and its dependencies in debug mode.
  destroy             Destroy all containers and volumes.
  flake8              Check for PEP8 compliance and other style issues.
  loaddata            Load data from file into database, only for development environment use.
  makemigrations      Perform makemigrations operation in Django.
  migrate             Perform migrate operation in Django.
  nbshell             Launch an interactive nbshell session.
  post-upgrade        Performs Nautobot common post-upgrade operations using a single entrypoint.
  restart             Gracefully restart all containers.
  start               Start Nautobot and its dependencies in detached mode.
  stop                Stop Nautobot and its dependencies.
  tests               Run all tests and linters.
  unittest            Run Nautobot unit tests.
  unittest-coverage   Report on code test coverage as measured by 'invoke unittest'.
  vscode              Launch Visual Studio Code with the appropriate Environment variables to run in a container.

Using Docker with Invoke

A development environment can be easily started up from the root of the project using the following commands:

  • invoke build - Builds Nautobot docker images
  • invoke migrate - Performs database migration operation in Django
  • invoke createsuperuser - Creates a superuser account for the Nautobot application
  • invoke debug - Starts Docker containers for Nautobot, PostgreSQL, Redis, Celery, and the RQ worker in debug mode and attaches their output to the terminal in the foreground. You may enter Control-C to stop the containers.

Additional useful commands for the development environment:

  • invoke start - Starts all Docker containers to run in the background with debug disabled
  • invoke stop - Stops all containers created by invoke start


To learn about advanced use cases within the Docker Compose workflow, see the Docker Compose Advanced Use Cases page.


If you are making edits to Nautobot's documentation in the Docker Compose workflow or otherwise needing to serve the docs locally, it is necessary to run a Python virtual environment:

  • Follow the steps in the Nautobot docs to install poetry
  • poetry shell
  • poetry install
  • mkdocs serve

Proceed to the Working in your Development Environment section

Python Virtual Environment Workflow

This workflow uses Python and Poetry to work with your development environment locally. It requires that you install the required system dependencies on your system yourself.

There are a few things you'll need:

  • A Linux system or environment
  • A MySQL or PostgreSQL server, which can be installed locally per the documentation
  • A Redis server, which can also be installed locally
  • A supported version of Python
  • A recent version of Poetry

Install Poetry

Poetry is a tool for dependency management and packaging in Python. It allows you to declare the libraries your project depends on and it will manage (install/update/remove) them for you. It will also manage virtual environments automatically, and allow for publishing packages to the Python Package Index.

You may install Poetry in your user environment by running:

$ curl -sSL | python -

For detailed installation instructions, please see the official Poetry installation guide.

Install Hadolint

Hadolint is a tool used to validate and lint Dockerfiles to ensure we are following best practices. On macOS with Homebrew you can install Hadolint by running:

$ brew install hadolint

Creating a Python Virtual Environment

A Python virtual environment (or virtualenv) is like a container for a set of Python packages. A virtualenv allow you to build environments suited to specific projects without interfering with system packages or other projects. When installed per the documentation, Nautobot uses a virtual environment in production.

For Nautobot development, we have selected Poetry, which will transparently create a virtualenv for you, automatically install all dependencies required for Nautobot to operate, and will also install the nautobot-server CLI command that you will utilize to interact with Nautobot from here on out.

Bootstrap your virtual environment using poetry install:

$ poetry install


If you are doing development or testing using MySQL, you may quickly install the mysqlclient library along with Nautobot by running poetry install --extras mysql.

This will create automatically create a virtualenv in your home directory, which houses a virtual copy of the Python executable and its related libraries and tooling. When running Nautobot for development, it will be run using the Python binary at found within the virtualenv.

Once created, you may activate the virtual environment using poetry shell:

$ poetry shell
Spawning shell within /home/example/.cache/pypoetry/virtualenvs/nautobot-Ams_xyDt-py3.8

$ . /home/example/.cache/pypoetry/virtualenvs/nautobot-Ams_xyDt-py3.8/bin/activate
(nautobot-Ams_xyDt-py3.8) $

Notice that the console prompt changes to indicate the active environment. This updates the necessary system environment variables to ensure that any Python scripts are run within the virtual environment.

Observe also that the python interpreter is bound within the virtualenv:

(nautobot-Ams_xyDt-py3.8) $ which python

To exit the virtual shell, use exit:

(nautobot-Ams_xyDt-py3.8) $ exit

Working with Poetry

Poetry automatically installs your dependencies. However, if you need to install any additional dependencies this can be done with pip. For example, if you really like using ipython for development:

(nautobot-Ams_xyDt-py3.8) $ pip3 install ipython
Collecting ipython
  Using cached ipython-7.20.0-py3-none-any.whl (784 kB)

It may not always be convenient to enter into the virtual shell just to run programs. You may also execute a given command ad hoc within the project's virtual shell by using poetry run:

$ poetry run mkdocs serve

Check out the Poetry usage guide for more tips.

Configuring Nautobot


Unless otherwise noted, all following commands should be executed inside the virtualenv.


Use poetry shell to enter the virtualenv.

Nautobot's configuration file is

Initializing a Config

You may also initialize a new configuration using nautobot-server init:

$ nautobot-server init
Configuration file created at '/home/example/.nautobot/'

You may also specify alternate file locations. Please refer to Configuring Nautobot for how to do that.

Using the Development Config

A suitable for development purposes can be found at development/ You may customize the values there or utilize environment variables to override the default values.

If you want to use this file, initialize a config first, then copy this file to the default location Nautobot expects to find its config:

$ cp development/ ~/.nautobot/
Required Settings

A newly created configuration includes sane defaults. If you need to customize them, edit your and update the following settings as required:

  • ALLOWED_HOSTS: This can be set to ["*"] for development purposes and must be set if DEBUG=False
  • DATABASES: Database connection parameters, if different from the defaults
  • Redis settings: Redis configuration requires multiple settings including CACHEOPS_REDIS and RQ_QUEUES. The defaults should be fine for development.
  • DEBUG: Set to True to enable verbose exception logging and, if installed, the Django debug toolbar
  • EXTRA_INSTALLED_APPS: Optionally provide a list of extra Django apps/plugins you may desire to use for development

Working in your Development Environment

Below are common commands for working your development environment.

Creating a Superuser

You'll need to create a administrative superuser account to be able to log into the Nautobot Web UI for the first time. Specifying an email address for the user is not required, but be sure to use a very strong password.

Docker Compose Workflow Virtual Environment Workflow
invoke createsuperuser nautobot-server createsuperuser

Starting the Development Server

Django provides a lightweight HTTP/WSGI server for development use. The development server automatically reloads Python code for each request, as needed. You don’t need to restart the server for code changes to take effect. However, some actions like adding files don’t trigger a restart, so you’ll have to restart the server in these cases.


DO NOT USE THIS SERVER IN A PRODUCTION SETTING. The development server is for development and testing purposes only. It is neither performant nor secure enough for production use.

You can start the Nautobot development server with the invoke start command (if using Docker), or the nautobot-server runserver management command:

Docker Compose Workflow Virtual Environment Workflow
invoke start nautobot-server runserver

For example:

$ nautobot-server runserver
Performing system checks...

System check identified no issues (0 silenced).
November 18, 2020 - 15:52:31
Django version 3.1, using settings 'nautobot.core.settings'
Starting development server at
Quit the server with CONTROL-C.


Do not use poetry run nautobot-server runserver as it will crash unless you also pass the --noreload flag, which somewhat defeats the purpose of using the development server. It is recommended to use nautobot-server runserver from within an active virtualenv (e.g. poetry shell). This is a known issue with Django and Poetry.

Please see the official Django documentation on runserver for more information.

You can then log into the development server at localhost:8080 with the superuser you created.

Starting the Interactive Shell

Nautobot provides an interactive Python shell that sets up the server environment and gives you direct access to the database models for debugging. Nautobot extends this slightly to automatically import models and other utilities.

Run the Nautobot interactive shell with invoke nbshell (Docker) or the nautobot-server nbshell management command:

Docker Compose Workflow Virtual Environment Workflow
invoke nbshell nautobot-server nbshell

For example:

$ nautobot-server nbshell
### Nautobot interactive shell (localhost)
### Python 3.9.1 | Django 3.1.3 | Nautobot 1.0.0b1
### lsmodels() will show available models. Use help(<model>) for more info.

Post-upgrade Operations

There will be times where you're working with the bleeding edge of Nautobot from the develop branch or feature branches and will need to pull in database changes or run server operations.

Get into the habit of running nautobot-server post_upgrade (or invoke post-upgrade when using Docker) after you pull in a major set of changes from Nautobot, which performs a handful of common operations (such as migrate) from a single command:

Docker Compose Workflow Virtual Environment Workflow
invoke post-upgrade nautobot-server post_upgrade

Please see the documentation on the nautobot-server post_upgrade command for more information.

Reinstalling Nautobot


This mostly applies to working with Nautobot in a virtualenv, since Docker containers are typically rebuilt when the code changes.

Sometimes when files are renamed, moved, or deleted and you've been working in the same environment for a while, you can encounter weird behavior. If this happens, don't panic and nuke your environment.

First, use pip3 to explicitly uninstall the Nautobot package from the environment:

$ pip3 uninstall -y nautobot
Found existing installation: nautobot 1.0.0b2
Uninstalling nautobot-1.0.0b2:
  Successfully uninstalled nautobot-1.0.0b2

Then try to just have Poetry do the right thing by telling it to install again:

$ poetry install
Installing dependencies from lock file

No dependencies to install or update

Installing the current project: nautobot (1.0.0-beta.2)

Running Tests

Throughout the course of development, it's a good idea to occasionally run Nautobot's test suite to catch any potential errors. Tests come in two primary flavors: Unit tests and integration tests.

Unit Tests

Unit tests are automated tests written and run to ensure that a section of the Nautobot application (known as the "unit") meets its design and behaves as intended and expected. Most commonly as a developer of or contributor to Nautobot you will be writing unit tests to exercise the code you have written. Unit tests are not meant to test how the application behaves, only the individual blocks of code, therefore use of mock data and phony connections is common in unit test code. As a guiding principle, unit tests should be fast, because they will be executed quite often.

By Nautobot convention, unit tests must be tagged with unit. The base test case class nautobot.utilities.testing.TestCase has this tag, therefore any test cases inheriting from that class do not need to be explicitly tagged. All existing view and API test cases in the Nautobot test suite utilities inherit from this class.


New unit tests must always inherit from nautobot.utilities.testing.TestCase. Do not use django.test.TestCase.


from django.test import TestCase

class MyTestCase(TestCase):


from nautobot.utilities.testing import TestCase

class MyTestCase(TestCase):

Unit tests are run using the invoke unittest command (if using the Docker development environment) or the nautobot-server test command:

Docker Compose Workflow Virtual Environment Workflow
invoke unittest nautobot-server --config=nautobot/core/tests/ test nautobot


By default invoke unittest will start and run the unit tests inside the Docker development container; this ensures that PostgreSQL and Redis servers are available during the test. However, if you have your environment configured such that nautobot-server can run locally, outside of the Docker environment, you may wish to set the environment variable INVOKE_NAUTOBOT_LOCAL=True to execute these tests in your local environment instead. See the Invoke configuration for more information.

In cases where you haven't made any changes to the database (which is most of the time), you can append the --keepdb argument to this command to reuse the test database between runs. This cuts down on the time it takes to run the test suite since the database doesn't have to be rebuilt each time.

Docker Compose Workflow Virtual Environment Workflow
invoke unittest --keepdb nautobot-server --config=nautobot/core/tests/ test --keepdb nautobot


Using the --keepdb argument will raise errors if you've modified any model fields since the previous test run.


In some cases when tests fail and exit uncleanly it may leave the test database in an inconsistent state. If you encounter errors about missing objects, remove --keepdb and run the tests again.

Integration Tests

Integration tests are automated tests written and run to ensure that the Nautobot application behaves as expected when being used as it would be in practice. By contrast to unit tests, where individual units of code are being tested, integration tests rely upon the server code actually running, and web UI clients or API clients to make real connections to the service to exercise actual workflows, such as navigating to the login page, filling out the username/passwords fields, and clicking the "Log In" button.

Integration testing is much more involved, and builds on top of the foundation laid by unit testing. As a guiding principle, integration tests should be comprehensive, because they are the last mile to asserting that Nautobot does what it is advertised to do. Without integration testing, we have to do it all manually, and that's no fun for anyone!

Running integrations tests requires the use of Docker at this time. They can be directly invoked using nautobot-server test just as unit tests can, however, a headless Firefox browser provided by Selenium is required. Because Selenium installation and setup is complicated, we have included a configuration for this to work out of the box using Docker.

The Selenium container is running a standalone, headless Firefox "web driver" browser that can be remotely controlled by Nautobot for use in integration testing.

Before running integration tests, the selenium container must be running. If you are using the Docker Compose workflow, it is automatically started for you. For the Virtual Environment workflow, you must start it manually.

Docker Compose Workflow Virtual Environment Workflow
(automatic) invoke start --service selenium

By Nautobot convention, integration tests must be tagged with integration. The base test case class nautobot.utilities.testing.integration.SeleniumTestCase has this tag, therefore any test cases inheriting from that class do not need to be explicitly tagged. All existing integration test cases in the Nautobot test suite utilities inherit from this class.


New integration tests must always inherit from nautobot.utilities.testing.integration.SeleniumTestCase and added in the integration directory in the tests directory of an inner Nautobot application. Do not use any other base class for integration tests.

We never want to risk running the unit tests and integration tests at the same time. The isolation from each other is critical to a clean and managable continuous development cycle.


from django.contrib.staticfiles.testing import StaticLiveServerTestCase

class MyIntegrationTestCase(StaticLiveServerTestCase):


from nautobot.utilities.testing.integration import SeleniumTestCase

class MyIntegrationTestCase(SeleniumTestCase):

Integration tests are run using the invoke integration-test command. All integration tests must inherit from nautobot.utilities.testing.integration.SeleniumTestCase, which itself is tagged with integration. A custom test runner has been implemented to automatically skip any test case tagged with integration by default, so normal unit tests run without any concern. To run the integration tests the --tag integration argument must be passed to nautobot-server test.

Docker Compose Workflow Virtual Environment Workflow
invoke integration-test nautobot-server --config=nautobot/core/tests/ test --tag integration nautobot


The same arguments supported by invoke unittest are supported by invoke integration-test. The key difference being the dependency upon the Selenium container, and inclusion of the integration tag.


You may also use invoke integration-test in the Virtual Environment workflow given that the selenium container is running, and that the INVOKE_NAUTOBOT_LOCAL=True environment variable has been set.

Customizing Integration Test Executions

The following environment variables can be provided when running tests to customize where Nautobot looks for Selenium and where Selenium looks for Nautobot. If using the default setup documented above, there is no need to customize these.

  • NAUTOBOT_SELENIUM_URL - The URL used by the Nautobot test runner to remotely control the headless Selenium Firefox node. You can provide your own, but it must be a Remote WebDriver. (Default: http://localhost:4444/wd/hub; for Docker: http://selenium:4444/wd/hub)
  • NAUTOBOT_SELENIUM_HOST - The hostname used by the Selenium WebDriver to access Nautobot using Firefox. (Default: host.docker.internal; for Docker: nautobot)

Verifying Code Style

To enforce best practices around consistent coding style, Nautobot uses Flake8 and Black. You should run both of these commands and ensure that they pass fully with regard to your code changes before opening a pull request upstream.

Docker Compose Workflow Virtual Environment Workflow
invoke flake8 flake8
invoke black black

Working on Documentation

Some features require documentation updates or new documentation to be written. The documentation files can be found in the docs directory. To preview these changes locally, you can use mkdocs.

Installing mkdocs

If you are using the poetry-based workflow, mkdocs should already be installed in your environment. This section mostly applies if you are using Docker to manage your development environment.

The mkdocs command can be installed via pip, either globally or in your virtual environment.

$ pip3 install mkdocs mkdocs-include-markdown-plugin

Writing Documentation

Once the mkdocs command has been installed, you can preview the documentation using mkdocs serve, which should start a web server at http://localhost:8001.

Documentation is written in Markdown. If you need to add additional pages or sections to the documentation, you can add them to mkdocs.yml at the root of the repository.

Submitting Pull Requests

Once you're happy with your work and have verified that all tests pass, commit your changes and push it upstream to your fork. Always provide descriptive (but not excessively verbose) commit messages. When working on a specific issue, be sure to reference it.

$ git commit -m "Closes #1234: Add IPv5 support"
$ git push origin

Once your fork has the new commit, submit a pull request to the Nautobot repo to propose the changes. Be sure to provide a detailed accounting of the changes being made and the reasons for doing so.

Once submitted, a maintainer will review your pull request and either merge it or request changes. If changes are needed, you can make them via new commits to your fork: The pull request will update automatically.


Remember, pull requests are entertained only for accepted issues. If an issue you want to work on hasn't been approved by a maintainer yet, it's best to avoid risking your time and effort on a change that might not be accepted.


Below are common issues you might encounter in your development environment and how to address them.

FATAL: sorry, too many clients already

When using nautobot-server runserver to do development you might run into a traceback that looks something like this:

Exception Type: OperationalError at /extras/tags/
Exception Value: FATAL:  sorry, too many clients already

The runserver development server is multi-threaded by default, which means that every request is creating its own connection. If you are doing some local testing or development that is resulting in a lot of connections to the database, pass --nothreading to the runserver command to disable threading:

$ nautobot-server runserver --nothreading