A plugin for Nautobot extending the core models with BGP-specific models.
New models enable modeling and management of BGP peerings, whether or not the peer device is present in Nautobot.
The initial development of this plugin was sponsored by Riot Games, Inc.
Navigate to Data Models for detailed descriptions on additional data models provided in the plugin.
To make the start with the plugin easier, we provide two example use cases for common OS platforms: Cisco and Juniper.
Navigate to Cisco Example Use Case for detailed instructions how to consume BGP Models plugin on Cisco devices.
Navigate to Juniper Example Use Case for detailed instructions how to consume BGP Models plugin on Juniper devices.
The plugin is available as a Python package in PyPI and can be installed with pip
:
pip install nautobot-bgp-models
The plugin is compatible with Nautobot 1.3 and higher
To ensure Nautobot BGP Models Plugin is automatically re-installed during future upgrades, create a file named local_requirements.txt
(if not already existing) in the Nautobot root directory (alongside requirements.txt
) and list the nautobot-bgp-models
package:
# echo nautobot-bgp-models >> local_requirements.txt
Once installed, the plugin needs to be enabled in your nautobot_config.py
# In your configuration.py
PLUGINS = ["nautobot_bgp_models"]
PLUGINS_CONFIG = {
"nautobot_bgp_models": {
"default_statuses": {
"AutonomousSystem": ["active", "available", "planned"],
"Peering": ["active", "decommissioned", "deprovisioning", "offline", "planned", "provisioning"],
}
}
}
In the default_statuses
section, you can define a list of default statuses to make available to AutonomousSystem
and/or Peering
. The lists must be composed of valid slugs of existing Status objects.
Pull requests are welcomed and automatically built and tested against multiple version of Python and multiple version of Nautobot through TravisCI.
The project is packaged with a light development environment based on docker-compose
to help with the local development of the project and to run the tests within TravisCI.
The project is following Network to Code software development guideline and is leveraging:
- Black, Pylint, Bandit and pydocstyle for Python linting and formatting.
- Django unit test to ensure the plugin is working properly.
For any questions or comments, please check the FAQ first and feel free to swing by the Network to Code slack channel (channel #networktocode). Sign up here