Exploring GitHub Copilot and Tabnine for Network Automation

Writing network automation scripts used to mean hours of staring at blank editors, searching Stack Overflow, and carefully crafting loops to pull data from routers. AI coding assistants have changed that workflow dramatically. Two tools dominate the conversation right now: GitHub Copilot and Tabnine. Both sit inside your editor and suggest code as you type, but they take very different approaches. Let's break down how each performs when you are actually writing IT scripts for network automation.

What Each Tool Is (and Is Not)

GitHub Copilot is a cloud-based AI assistant built by GitHub and OpenAI. It is powered by large language models trained on billions of lines of public code. It integrates with VS Code, JetBrains IDEs, Neovim, and Visual Studio. Pricing starts at $10/month for individuals.

Tabnine is an AI code completion tool that has been around longer. Its key differentiator is flexibility: it can run models locally on your machine, making it popular in enterprise environments where sending code to external servers is a compliance concern. It supports nearly every major IDE and offers a free tier with limited completions.

Setting Up for Network Automation Work

For both tools, your environment matters. Most network automation is written in Python, using libraries like netmiko, napalm, nornir, or paramiko. The richer your project context (existing files, imports, comments), the better both tools perform.

A simple setup in VS Code for GitHub Copilot:

1. Install the GitHub Copilot extension from the VS Code marketplace.
2. Sign in with your GitHub account.
3. Open a Python file and start typing; suggestions appear inline.

Tabnine setup follows the same pattern. Install the Tabnine extension, create an account, and choose whether you want cloud or local model inference.

Head-to-Head: Writing a Network Script

Let's use a concrete example. You want a script that connects to a list of Cisco routers, pulls the running configuration, and saves each one to a file. Start with a comment describing the task and watch what each tool produces.

With GitHub Copilot, typing a comment like the one below almost immediately produces a complete, functional function body using netmiko:

# Connect to each router in the inventory and save the running config to a file
import json
from netmiko import ConnectHandler

def backup_configs(inventory_file):
    with open(inventory_file) as f:
        devices = json.load(f)

    for device in devices:
        connection = ConnectHandler(**device)
        config = connection.send_command("show running-config")
        filename = f"{device['host']}_config.txt"
        with open(filename, "w") as config_file:
            config_file.write(config)
        connection.disconnect()
        print(f"Saved config for {device['host']}")

Copilot inferred the right library, handled file I/O, and even added a print statement for feedback. That is representative of how it performs on well-described tasks.

Tabnine tends to produce shorter, more conservative completions. It excels at completing what you have already started rather than generating large blocks from a comment alone. If you type connection = ConnectHandler(, Tabnine will correctly suggest your parameter names based on the surrounding code. It is more of a fast, intelligent autocomplete than a code generator.

Where Each Tool Shines for IT Professionals

• GitHub Copilot: Best for generating boilerplate IT scripts, writing functions from scratch based on descriptive comments, and working with well-known libraries like netmiko and nornir.
• Tabnine: Best for teams with strict data privacy requirements, developers who want a locally-run model, and speeding up completion of partially-written network automation code.
• GitHub Copilot Chat: Copilot now includes a chat interface where you can ask questions like "What does this OSPF configuration block do?" directly inside VS Code, adding a troubleshooting layer that Tabnine does not match.
• Tabnine Teams: Allows you to fine-tune the model on your organization's internal codebase, which can dramatically improve suggestions for proprietary scripts and tools.

A Practical Recommendation

If you are an individual network engineer or working in a small team without strict data governance rules, start with GitHub Copilot. The quality of its multi-line generation for network automation tasks is noticeably stronger, and the chat feature adds real value when debugging scripts. If your organization restricts sending source code to third-party cloud services, Tabnine's local model option makes it the practical choice. The two are not mutually exclusive; some engineers use Tabnine for sensitive work and Copilot for personal or lab projects.

What's Next

Now that you have a feel for how AI coding assistants handle network automation scripts, the next step is understanding how to write better prompts to get higher-quality output. In the next post, we cover prompt engineering techniques specifically for IT professionals, showing you how to guide tools like Copilot to produce cleaner, more reliable code on the first attempt.