Pin It!

The limitations of MicroPython nearly killed my project—until I started using them as design constraints.

I was deep into a rapid prototyping sprint—ESP32 board on the desk, sensors wired, MicroPython flashed. The first few scripts ran beautifully… until I tried to push just a little further. A few new features, some multitasking, a little data buffering—and suddenly the board was choking, freezing, or rebooting at random.

I assumed something was broken. Then I realized: I’d hit the limitations of MicroPython.

At first, it felt like a dead end. But in true FOSS spirit, I looked closer. What I thought were flaws were actually boundaries—and once I stopped fighting them, I started designing around them. The result? A leaner, smarter system that still did everything I needed, just with fewer assumptions.

This guide walks through the real limitations of MicroPython on ESP32—and more importantly, how to work with them, not against them.

If you’ve ever felt like switching to C just to escape a bug, this one’s for you.

1. Understanding Memory Limits in MicroPython

One of the first bottlenecks beginners hit is memory—not because their code is massive, but because MicroPython on the ESP32 runs in a relatively tiny heap. You might have 512KB of heap space, and that includes all your strings, data structures, and imported modules. It’s also garbage-collected, which means memory use can spike unpredictably—especially during loops or large data reads.

FOSS Pro Tip:
Run gc.mem_free() in your script to monitor memory consumption. For larger data handling, consider offloading to a lower-level C module or simplify your architecture by breaking the task into smaller, stateless chunks.

· · ─ ·𖥸· ─ · ·

2. Limited Performance for Intensive Tasks

One of the most significant limitations of MicroPython on the ESP32 is its performance. As an interpreted language, MicroPython executes code more slowly compared to compiled languages like C or C++. This means tasks requiring high-speed processing or real-time performance, such as audio signal processing or fast data acquisition, may exceed its capabilities.

Example:

• Applications requiring consistent timing (e.g., PWM or real-time motor control) might experience delays due to the overhead of the Python interpreter.

Alternative: For such tasks, developers often resort to C/C++ using the ESP-IDF (Espressif IoT Development Framework) for optimal performance.

· · ─ ·𖥸· ─ · ·

3. Limited Memory Management

MicroPython’s memory footprint is another constraint. While the ESP32 offers more memory than many microcontrollers, MicroPython’s runtime and libraries consume a significant portion of it. This leaves less memory available for user code, which can lead to memory allocation errors, especially in complex applications or when handling large datasets.

Example:

• Applications requiring heavy computation or handling large arrays of data may encounter out-of-memory errors.

Alternative: Optimize memory usage by writing critical parts of the application in C and integrating them with MicroPython via modules.

· · ─ ·𖥸· ─ · ·

4. Limited Access to Low-Level Features

MicroPython abstracts many hardware interactions, which simplifies development but restricts access to advanced features of the ESP32. For instance, tasks like configuring specific registers or using low-level peripherals (e.g., certain DMA operations) may not be fully supported or require custom modules.

Example:

• Advanced networking features or fine-tuned control of the ESP32’s dual-core architecture may not be easily accessible.

Alternative: Use ESP-IDF or Arduino frameworks for projects requiring granular control over hardware.

· · ─ ·𖥸· ─ · ·

5. Limited Library Support

While MicroPython includes many built-in libraries for common tasks, it lacks the extensive ecosystem of libraries available for standard Python or lower-level frameworks like ESP-IDF. This limitation can hinder development if your project relies on specialized libraries or third-party hardware drivers.

Example:

• Specific sensors or communication modules may lack native support, requiring developers to write custom drivers.

Alternative: Evaluate whether the required libraries are supported in MicroPython or consider using a language with a broader ecosystem.

· · ─ ·𖥸· ─ · ·

6. Concurrency and Multithreading Challenges

The ESP32’s dual-core architecture supports multitasking, but MicroPython’s implementation of concurrency is limited. While it supports lightweight threading via “uasyncio,” it doesn’t provide true multithreading or make full use of both cores. This can be a bottleneck for applications requiring parallel processing.

Example:

• Applications needing simultaneous Wi-Fi communication and sensor data processing may face scheduling delays or reduced efficiency.

Alternative: For true multithreading, use ESP-IDF to leverage both cores effectively.

· · ─ ·𖥸· ─ · ·

7. Limited Community Support Compared to Alternatives

Although MicroPython has an active community, it is smaller than that of traditional Python or ESP-IDF. This can lead to fewer resources, tutorials, and examples for specific problems, which may slow down troubleshooting and development.

Example:

• When encountering a niche issue or bug, finding a solution might take more time compared to using more established frameworks.

Alternative: Check the official MicroPython documentation and forums or consider switching to a framework with broader community support.

· · ─ ·𖥸· ─ · ·

8. There’s No Real Threading—But Async Works (Mostly)

MicroPython includes a thread module, but it’s mostly a tease. On the ESP32, you’ll quickly find that true parallel threads either don’t work or destabilize the board. Instead, use uasyncio for cooperative multitasking—it’s a lean version of Python’s async event loop, perfect for blinking LEDs while listening for sensor input.

Be aware: uasyncio isn’t fully compatible with all blocking I/O. You’ll need to write non-blocking code or use await wisely.

Beginner Tip:
Think of it like this: You’re not “multi-threading”—you’re taking turns quickly, like a tiny round-robin task scheduler that runs on politeness.

· · ─ ·𖥸· ─ · ·

8. ESP32 Peripheral Features You Can’t Touch (Yet)

MicroPython doesn’t expose all of ESP32’s hardware capabilities. For instance:

• I2S audio support is missing or experimental.
• CAN bus? Nope—not out of the box.
• Hardware PWM is available, but only on certain pins, and not as finely tunable as in ESP-IDF or Arduino C++.

This can be a dealbreaker for advanced applications like audio streaming or automotive prototyping.

Workaround:
Use C modules and bind them with ffi if you’re determined—or scope your project goals to what MicroPython does well: automation, scripting, sensor integration, and Wi-Fi + MQTT tasks.

· · ─ ·𖥸· ─ · ·

Conclusion: MicroPython Isn’t Broken—It’s Just Honest

Why Knowing Your Limits Leads to Better Code

The truth is, most of the limitations of MicroPython are predictable—if you know where to look. Yes, it lacks real threads. No, it doesn’t handle huge data pipelines or ultra-low-level hardware hacks well. But it does shine in fast prototyping, teaching, automation scripts, and FOSS-tinkering—especially when used with intent.

Instead of seeing its constraints as reasons to switch languages, I’ve learned to treat them as design guidelines. Keep things small. Write modular code. Think asynchronously. In the end, you don’t just get a working project—you get a better understanding of how your system behaves under real-world pressure.

If this article saved you from rewriting your codebase or helped you ship faster with less, don’t miss the next one.

Subscribe now at https://www.samgalope.dev/newsletter/ for more grounded insights, dev tricks, and FOSS-first engineering advice.

Let’s build smarter. Together.