We tried really hard not to make this an AI-only list.

Seriously.

Hello 👋

We’re back with the 11th edition of our annual Top Python Libraries, after spending way too many hours reviewing, testing, and debating what actually deserves a spot this year.

With AI, LLMs, and agent frameworks stealing the spotlight, it would’ve been very easy (and honestly very tempting) to publish a list that was 90% AI.

Instead, we kept the same structure:

• General Use — the foundations teams still rely on every day

• AI / ML / Data — the tools shaping how modern systems are built

Because real-world Python stacks don’t live in a single bucket.

Our team reviewed hundreds of libraries, prioritizing:

• Real-world usefulness (not just hype)

• Active maintenance

• Clear developer value

👉 Read the full article:

General Use

1. - a blazing-fast type checker built in Rust

2. - measures how hard it is to understand the code

3. - extracts data from 50+ file formats

4. - control request rates with five algorithms

5. - timing HTTP requests with waterfall views

6. - security middleware for FastAPI apps

7. - seamlessly enhance modules without monkey-patching

8. - executable specs that generate working code

9. - detects dead code and security vulnerabilities

10. - easy OpenAPI docs for any framework

AI / ML / Data

1. & - connect LLMs to external data sources

2. - compact JSON encoding for LLMs

3. - framework for building sophisticated LLM agents

4. - agent framework that executes actions as code

5. - building complex AI workflows with ease

6. - unified batch processing for AI providers

7. - convert any file to clean Markdown

8. - AI-powered data exploration through natural language

9. - extract key details from any document

10. - bridging AI and geospatial data analysis

Huge respect to the maintainers behind these projects. Python keeps evolving because of your work.

Now your turn:

• Which libraries would you have included?

• Any tools you think are overhyped?

• What should we keep an eye on for 2026?

This list gets better every year thanks to community feedback. 🚀

For the time being, I have chosen not to use generative AI tools for programming, both at work and for hobby projects. I imagine that this puts me in the minority, but I'd love to hear from others who have a similar approach.

These are my main reasons for avoiding AI for the time being:

• I imagine that, if I made AI a central component of my workflow, my own ability to write and debug code . I think this risk outweighs the possible (but ) time-saving benefits of AI.

• AI models might inadvertently spit out large copies of copyleft code; thus, if I incorporated these into my programs, I might then need to release the entire program under a similar copyleft license. This would be frustrating for hobby projects and a potential nightmare for professional ones.

• I find the experience of writing my own code very fulfilling, and I imagine that using AI might take .

• LLMs rely on huge amounts of human-generated code and text in order to produce their output. Thus, even if these tools become ubiquitous, I think there will always be a need (and demand) for programmers who can write code without AI--both for training models and for fixing those models' mistakes.

• As Ed Zitron has pointed out, generative AI tools are losing tons of money at the moment, so in order to survive, they will most likely need to steeply increase their rates or offer a worse experience. This would be yet another reason not to rely on them in the first place. (On a related note, I try to use free and open-source tools as much as possible in order to avoid getting locked into proprietary vendors' products. This gives me another reason to avoid generative AI tools, as most, if not all of them, don't appear to fall into the FOSS category.)*

• Unlike calculators, compilers, interpreters, etc., generative AI tools are non-deterministic. If I can't count on them to produce the exact same output given the exact same input, I don't want to make them a central part of my workflow.**

I am fortunate to work in a setting where the choice to use AI is totally optional. If my supervisor ever required me to use AI, I would most likely start to do so--as having a job is more important to me than maintaining a particular approach. However, even then, I think the time I spent learning and writing Python without AI would be well worth it--as, in order to evaluate the code AI spits out, it is very helpful, and perhaps crucial, to know how to write that same code yourself. (And I would continue to use an AI-free approach for my own hobby projects.)

*A commenter noted that at least one LLM can run on your own device. This would make the potential cost issue less worrisome for users, but it does call into question whether the billions of dollars being poured into data centers will really pay off for AI companies and the investors funding them.

**The same commenter pointed out that you can configure gen AI tools to always provide the same output given a certain input, which contradicts my determinism argument. However, it's fair to say that these tools are still less predictable than calculators, compilers, etc. And I think it's this lack of predictability that I was trying to get at in my post.

i all, is it realistic to build an autonomous drone using Python/Micropython on a low budget?

The idea is not a high-speed or acrobatic drone, but a slow, autonomous system for experimentation, preferably a naval drone.

Has anyone here used Python/MicroPython in real robotics projects?

Thanks! appreciate any real-world experience or pointers.