Modern test systems don’t live in a vacuum... and neither should your LabVIEW code.

If you’re new to LabVIEW or wondering why it’s still used in 2025, read What LabVIEW Is Used For to understand where it fits in the test automation landscape.

If your current system is built in LabVIEW, it may still be doing the heavy lifting: managing hardware, running UIs, acquiring data. But the world around it has evolved. There are now better ways to script sequences, push data to the cloud, and connect with the rest of your tech stack.

The good news? You don’t need to rip and replace.

When to Bring in Python

LabVIEW excels at parallel execution and hardware integration. But for scripting, decision logic, and integration tasks → Python often makes more sense:

• Need to write complex test sequences? → Python is easier to read, edit, and version.
• Need to interface with REST APIs or databases? → Python’s libraries make it simple.
• Need to share logic with non-LabVIEW engineers? → Python keeps your team connected.

Instead of rewriting the system, embed a bidirectional LabVIEW-Python connector. Python handles flow; LabVIEW handles the instruments.

When to Use REST APIs

Modern test systems don’t just collect data, they push it to:

• Cloud dashboards (Grafana, Power BI)
• Traceability systems (MES, ERP)
• File shares, ticketing systems, and beyond

LabVIEW can speak HTTP, but Python does it more flexibly. A hybrid model works well: LabVIEW collects data → Python packages it then makes the API call.

Why LVLIBs Still Matter

In this world of integration and scripting, you might wonder if your LabVIEW code should be rewritten in classes or abstracted further.

Not necessarily. LVLIB-based architectures still offer the best tradeoff for test systems:

• Modular code organization
• Scoped visibility and encapsulation
• Simple dependency management

If you’re deciding between libraries and classes, LVLIB vs LVCLASS offers a deep dive on why I lean toward libraries for most real-world systems.

Combine that with clean queues, events, and a few scoped globals... and you get a powerful, maintainable core that integrates easily with modern tools.

Real-World Example

Let’s say you’re validating battery modules with 6 temperature-controlled chambers, and your team wants to:

• Configure tests from a web dashboard
• Run sequences that involve complex logic
• Log results to a database and generate a report

With a modernized setup:

• Python handles sequencing, control logic, and data storage
• LabVIEW handles hardware I/O, safety interlocks, and UI
• Data flows back to your dashboard via REST, GraphQL, or MQTT

No need to rewrite your LabVIEW base. Just enhance it.

Want to implement this hybrid approach? Don’t miss the step-by-step guide in Integrating LabVIEW and Python.

Final Thoughts

You don’t have to choose between “old LabVIEW” and “new everything else.” The future is hybrid (and that’s a good thing).