NOC Project VS trigger

Compare NOC Project vs trigger and see what are their differences.


Trigger is a robust network automation toolkit written in Python that was designed for interfacing with network devices. (by trigger)
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NOC Project trigger
0 1
- 547
- 0.0%
- 0.0
- over 1 year ago
- BSD 3-clause "New" or "Revised" License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

NOC Project

Posts with mentions or reviews of NOC Project. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning NOC Project yet.
Tracking mentions began in Dec 2020.


Posts with mentions or reviews of trigger. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-02-25.
  • Network to Code Nautobot
    5 projects | | 25 Feb 2021
    First of all: I am not a marketing guy. I'm a Python guy and a network guy, and I've been doing open source for many years with my projects [Trigger]( and [NSoT](

What are some alternatives?

When comparing NOC Project and trigger you can also consider the following projects:

Oxidized - Oxidized is a network device configuration backup tool. It's a RANCID replacement!

phpIPAM - phpipam development repository

ZeroTier - A Smart Ethernet Switch for Earth

nautobot - Network Source of Truth & Network Automation Platform

jazigo - Jazigo is a tool written in Go for retrieving configuration for multiple devices, similar to rancid, fetchconfig, oxidized, Sweet.

frr - The FRRouting Protocol Suite

Surprise - A Python scikit for building and analyzing recommender systems