The Hate on AI Agentic Libraries

Is it justified? or are we missing a room full of opportunties?

People hating on Agentic AI libraries are so annoying sometimes.

AI Agents are relatively new, and the whole ecosystem is figuring out how to do things. We are all learning from our experiences, and so are the makers of these libraries. Look at their versions—they've barely hit 1.0 yet. How can you expect a newborn baby to run already? An OSS tool will grow along with its users, like any other tool. Most of it is Open Source, which means you can contribute rather than stand on the sidelines and shout at the maintainers.

“I introduced agentic AI into my codebase two and a half weeks ago… today I’m scrapping it for parts—sort of.”
— r/ExperiencedDevs (recent thread)

But this behavior on the internet is not new. I understand how something so glorified is not working as expected. Let me walk you through two major tech waves I was a part of

Django

Django was one of the biggest splashes of the Python ecosystem, but it wasn’t adopted overnight. Beginners swear off it mid-install because even “hello world” takes hours. People say DRF is over-engineered. They gripe about magic, opaque class hierarchies, and that admin panel they didn’t ask for.

“Django has a steep learning curve.”
— Hacker News, 2019

“I’m finding it quite hard to master django (rest framework as well)… Django is complicated even if you know Python. Django has the highest learning curve of any framework, by orders of magnitude.”
— r/learnpython, 2020

But time smoothed that out. Today, Django runs enterprise apps, powers entire APIs, and still filters in fresh students at bootcamps.

Cloud Computing

Cloud computing got dragged hard when it launched:

• In 2008, Richard Stallman called the cloud “worse than stupidity,” dead-set on defending your machine over someone else’s server.

• Oracle’s CEO, Larry Ellison, labeled it “fashion-driven gibberish” even as cloud startups scaled into billions.

These were not fringe opinions. They came from the top echelon. People griped about cost unpredictability, vendor lock-in, service sprawl, and debugging nightmares, yet the cloud became the backbone of global infrastructure.

But take a look around now. Google, AWS, and Azure have significant market share. Everyone builds abstractions after abstractions, be it Vercel, Netlify, or whatever.

“The biggest frustration… overwhelmed with scores of settings and options you need to fill in before provisioning anything.”
— r/devops

“Amazon’s EBS is a barrel of laughs in terms of performance and reliability… constant source of failure…”
— Hacker News, 2011

Agentic AI Is Just the Next Chapter

Agentic AI is currently somewhere between Django’s “lost in MVT” era and the cloud’s “EBS down” phase. It’s underdeveloped, messy, and broken in places, but it’s growing fast.

Since most of these libraries are open-source, we have two options: We can sit on the sidelines and tweet snark, or we can build. Fork a repo, fix the docs, submit a PR, improve state caching or orchestration, and help shape the version 1.0 we’re all waiting for.

Because if you decide not to participate? That’s fine. But remember: the people who showed up when things sucked are the ones who become thought leaders of the ecosystem in the future.

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Some anti-thoughts and my answers to them

Yes, the hype is too much, but for most technological waves, it always was. Companies are making money off these so-called open-source libraries. If OSS needs to be genuinely free, it needs funding. Unfortunately, that’s not the case for major libraries. There is no harm in building a self-sustaining OSS library.

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Join the 30-Day online GenAI Challenge.

Are you looking at GenAI technologies from the sidelines, not knowing where to start? There are already too many resources, so which is the best one?

Worry not!

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