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Monitoring IP SLAs with Python, DNA Center, and NetBox

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Automate IP SLA monitoring across your network using Python, Cisco DNA Center APIs, and NetBox as your source of truth.

GitHub Repo: https://github.com/NetworkThinkTank-Labs/ip-sla-monitor

Introduction

If you manage a network of any size, you know that keeping tabs on performance metrics like latency, jitter, and packet loss is critical. Cisco IP SLA (Service Level Agreement) operations are the go-to feature for probing network paths and measuring these metrics directly from your routers and switches. But manually checking IP SLA statistics across dozens or hundreds of devices? That does not scale.

In this post, I will walk you through a Python-based tool I built that pulls IP SLA data from Cisco DNA Center via its REST API, enriches it with device metadata from NetBox, and generates automated performance reports. Whether you are running a handful of branch routers or a large enterprise campus, this approach gives you a scalable, repeatable way to monitor network performance.

What is IP SLA?

Cisco IP SLA is a built-in feature on Cisco IOS and IOS-XE devices that allows you to generate synthetic traffic to measure network performance. You can configure operations like ICMP echo (ping), UDP jitter, HTTP GET, and more. Each operation continuously measures metrics such as round-trip time (RTT), latency, jitter, packet loss, and availability. These metrics are essential for validating SLA compliance, troubleshooting performance issues, and capacity planning.

The Tools

This project brings together three key components. First, Python does the heavy lifting for API calls, data parsing, and report generation. Second, Cisco DNA Center provides a centralized REST API for pulling device inventory and running CLI commands across your entire network without SSH-ing into each device individually. Third, NetBox acts as our network source of truth, storing device metadata like site assignments, roles, platforms, and IP addresses that we use to enrich the raw SLA data.

How It Works

The IP SLA Monitor tool follows a simple three-step workflow:

  1. Authenticate with DNA Center and pull IP SLA operation statistics from all monitored devices using the command-runner API.
  2. 2. Query NetBox for each device to enrich the data with site name, device role, platform, and management IP.
  3. 3. Evaluate each SLA operation against configurable thresholds for latency, jitter, and packet loss, then generate JSON and CSV reports.
  4. The tool can run as a one-shot collection or in a continuous monitoring loop with a configurable polling interval. Alerts are logged to the console when any operation exceeds your defined thresholds.

The Python Code

The project is organized into three main scripts:

ip_sla_monitor.py is the main orchestration script that ties everything together. It loads configuration from a .env file, initializes the DNA Center and NetBox clients, collects SLA data, enriches it, evaluates thresholds, and saves reports.

dnac_integration.py handles all communication with the Cisco DNA Center REST API including authentication, device inventory retrieval, and IP SLA data collection via the command-runner API.

netbox_integration.py connects to the NetBox API to look up device metadata by hostname, returning site assignments, device roles, platform types, and IP addresses.

Getting Started

Getting up and running is straightforward. Clone the repository, set up a virtual environment, install the dependencies from requirements.txt, and configure your .env file with your DNA Center and NetBox credentials. The only Python packages required are requests for HTTP API calls, python-dotenv for environment variable management, and urllib3. No complex frameworks or heavy dependencies. Full setup instructions are in the GitHub repo README.

Threshold Alerting

One of the most useful features is configurable threshold alerting. You define your acceptable limits for latency, jitter, and packet loss in the .env file, and the tool flags any SLA operation that exceeds those limits. For example, with default thresholds of 100ms latency, 30ms jitter, and 1% packet loss, a branch router showing 115ms latency and 2.1% packet loss would be immediately flagged as an alert in the console output and reports.

Sample Output

The tool generates both JSON and CSV reports. The JSON report includes a summary section with total operations, passing and failing counts, and average latency, followed by detailed per-operation data enriched with NetBox metadata. The CSV report provides the same data in a tabular format that you can easily import into Excel or feed into other monitoring tools. Sample output files are included in the GitHub repository under the output directory.

What is Next

This project is a solid foundation, but there is plenty of room to extend it. Some ideas for future enhancements include adding webhook or email notifications for alerts, integrating with Grafana for real-time dashboards, storing historical data in a time-series database like InfluxDB, and expanding the command-runner integration to pull live SLA statistics directly from devices.

Wrapping Up

Python automation combined with APIs from DNA Center and NetBox gives network engineers a powerful toolkit for monitoring IP SLAs at scale. Instead of manually checking IP SLA stats on individual devices, you can automate the entire workflow and get enriched reports in minutes.

Check out the full source code, sample outputs, and setup instructions in the GitHub repository: https://github.com/NetworkThinkTank-Labs/ip-sla-monitor

If you found this useful, check out my other posts on network automation including Automating Network Device Backups with Python and Netmiko and Build a Home Lab Like a Pro. Stay tuned for more content from the NetworkThinkTank!