This is a cool project, and the idea of using LLMs to selectively extract features from open source projects is an interesting concept.

The only thing I take issue with is the phrase "LiteLLM Without the Bloat." A lot of the features that have been removed (like cost tracking, streaming, caching) are... kind of the core value proposition of LiteLLM for many of their users.

IIRC it's 700mb with all deps included, to me that's clear bloat given you could accomplish it with a fraction of that

I guess they got that message so they're porting to rust!

https://docs.litellm.ai/blog/litellm-rust-launch

We run litellm at work and not once anybody cared about those 700mb.

LiteLLM doesn't quite live up to its name. With all those features, there is nothing "lite" about it. It is essential for a project to live up to its name.

Imagine Sqlite adding heavy features from Postgresql, e.g. row-level security.

But imagine Sqlite not supporting joins or window functions... sure they are useful but look how many LOC it adds! Who is the arbiter of what Lite actually means?

LiteLLM's problem isn't really features, it's how bloated all the features are, and specifically how AI maximalist and janky their dev practices are.

We run it at my org and it's never been a noticeable resource hog. It's actually the best performer between it, our AI observability stack and the front end.

LiteLLM is vibecoded trash.

>> kind of the core value proposition of LiteLLM for many of their users.

THIS! And it's way cheaper than others like Kong =)

We ran the LiteLLM proxy for months mostly for per-team budgets. Spend tracking was the only reason we kept it. Everything else we could've done with a 50-line wrapper. Kong's pricing would've been a non-starter for a team our size.

Where does it mention it selectively extract features from OSS projects?

I strongly recommend the authors rewrite the readme by hand. It’s kind of a snif test for how much care someone put into this project.

I have the same complaint about oh-my-pi's readme. The tone is obnoxious. It's somehow jaded and matter-of-fact at the same time. Like it was written by that one guy at work who never misses a chance to brag about how clever he is.

There is a band called The Protomen who do rock operas about the Mega Man storyline. They have a very earnest, gritty sound. Every time I read a project README written by AI, I hear the voice over from the first track of the first album, Act 1: Hope Rides Alone (https://youtu.be/VZ8jyGVioxg?is=gsXlgwa4Ubx6LmKE).

"Twelve years Light worked and on a cold night in the year 200X, Protoman was born. A perfect man, an unbeatable machine, hell-bent on destroying every evil standing between man and freedom, built for one purpose, to destroy Wily's army of evil robots. Ready. Willing. Prepared to fight."

For the Protomen it makes sense. But for a project README it's so absurdly melodramatic.

One "AI-ism" I noticed is that LLMs often just put sentences behind one another without using any connecting adverbs - and just leave it to the reader's imagination how the sentences are related to each other.

E.g. in the readme: "LiteLLM routes LLM calls across providers and translates between message formats. That core is buried under 100k+ LOC of proxy servers, caching layers [etc...]".

Those two sentences have opposite sentiment on LiteLLM, so a human author would at least put a "but" between them. In contrast, the LLM just strings them together.

This reads "blunt" and "matter-of-fact" at first glance, but I wonder if it's really just an artifact of allocating less space for text generation and more for code in LLMs.

omp is slopware through and through. And it shows, slow as hell to use

Agree. The LLM'isms are offputting.

This readme is better than most readmes. However they came to making it, it's clearly working

Throwing my support for this. Do not use LLMs to write things humans should write.

Really? I mean, yeah, it's probably written by an LLM, but it's hardly the worst that I've seen. Looks way more straight forward than the modern README featuring a ton of badges, emojis, confusing out-of-context screenshots, "trust me bro" installation instructions, vague elevator pitches, "used by netflix, nasa, disney, good morning america, alex jones, the church of scientology", and other verbiage to create the illusion that the author won't immediately get bored and abandon their glorified dissertation piece. They all scream "give me your github stars" whereas this one doesn't. But I still get what you mean when it comes to the particular 'isms.

One of the 2 dependencies, httpx, isn't really maintained anymore. Pydantic picked it up as httpx2: https://pydantic.dev/docs/httpx2

Funny, everything you pruned away is the reason I’m deploying LiteLLM in our platform. Having a reliable way to track token spend per customer across different services is important to us, and LiteLLM handles this well

I imagine many people code their own LLM client after getting fed up with the bad options out there. It’s very easy with ai coding tools.

I’m biased but I think mine is coded to a higher standard than litelm. https://github.com/s-banach/langchaint

It would be great if there were a plugin/extension infrastructure. For example, to write the cache and costs however and whenever I see fit

I like the API, and since this is open source I will copy the design with attribution: I have been organically hacking little bits of LLM client code for most providers in Common Lisp for about 3 1/2 years and it is time to clean up all my old code. I probably need to do the same sort of refactoring for my search API wrappers.

Thanks for the cool project.

First off, cool project! It's always great to see derivatives that question the efficiency of the established product.

I think the main thing the readme is missing is the core benefits. Reducing LOC and dependencies is cool, but it would be great to understand if this provides some additional benefits like lower latency or memory requirements.

ROTFL, I had to laugh so hard!

"Without the Bloat"

vs.

"litellm routes LLM calls across providers and translates between message formats. That core is buried under 100k+ LOC"

Seriously anybody considering 100k+ LOC not a bloat? You made my day!

Let's just say the author's and my definition of bloat is not the same. Full disclosure, I'm the guy who reimplemented etcher (over 400Mb) in a mere 300Kb, Capstone (over 1Mb) in only 66Kb and who compressed LPC charactersheets (over 700Mb) into 4Mb. That's my interpretation of "non-bloated".

You misunderstood. This new project has 2,900 LOC. Maybe the spelling change is too subtle.

No, you forgot about the dependencies (others said it to be 700Mb). And 2,900 LOC is still way too much for handling a remote API call, especially in a high level language such as Python where the biggest part of the task is delegated to separate modules.

I wrote an entire https client from ground up in 117 LOC (and in a low level language, not Python): https://gitlab.com/bztsrc/skrellm/-/blob/main/src/https.c

Again, this guy and me disagree on what "not a bloat" means.

We hit the same wall with litellm and ended up using the plain openai SDK with a base_url swap. Most providers speak that format now. Only Anthropic needed its own adapter, maybe 150 lines. Streaming and tool-call quirks are where it gets messy.

Cool project, I had to implement the same for my experimental AI Agent: https://github.com/quantized-ai/luca-py

LiteLLM and LangChain are AWFUL pieces of software and should be avoided at ALL costs.

Btw, you should update httpx to httpx2, and I think it's not much effort to remove openai's SDK compatibility.

I'd love to have a provider-agnostic LLM router (almost) dependency free (aside from httpx2).

Also Strongly recommend renaming to avoid confusion

Drop the Lite, it's cleaner, just use the godamn LLM directly.

the smaller surface is nice. i'd still keep auth and spend caps outside the proxy though, because once every app shares one key the blast radius gets ugly fast.

Immediate first impression is that this tagline should go. If the project brings something valuable to the table it doesn't need to shit on other permissively licensed open source projects to make a case for itself

most software like this will be dematerialized, democratized, and demonetized - companies building in the infra band being increasingly disintermediated

Make a docker image rootless please, thanks

This is a 30 minute project with a frontier LLM. I don’t see why anyone would use anyone else’s router. Techniques are valuable today. Libraries are not.

Writing it takes 30 minutes, sure. Maintaining it is the part nobody counts. Providers change streaming formats, tool-call shapes and error codes every few months, and you'll find out from a customer. Who's on the hook for that when it's your own router?

Actually not. There are so many edge cases. Also these routers are only useful if they have a minimal layer of observability.

Yes, LLMs can do a great job at writing semi-working MVP. Turning it into a usable project still requires a team.

Yeah, maybe for your toy project you can use a LLM written tool.

Also, I am not saying LiteLLM is good either.

Has anyone actually measured where the edge cases bite? My guess is streaming tool calls and provider-specific error/retry semantics, not the happy path. Those only show up after a month in prod, which is exactly when a 30 minute rewrite stops looking cheap.

There are always people who need an entire team to produce something like OP repo. Enterprise FizzBuzz is real after all.

token counting is useful though. There's also other ways to reduce size, ie a plugin API.

This readme, when it talks about all the different AI endpoints it can use, reminds me of something.

I'm not an AI bro, but I've dabbled. It's kind of remarkable that all the different providers speak the same "openai compatibile" https endpoints. In other realms of software development, real interoperability like that can be kind of rare. Even if people support conceptually the same API, everybody always puts their unique incompatible spin on it. In the dabbling that I've done, big incompatibilities seem rare.

How does it compare to Bifrost?

Lot of that "bloat" is provider quirks someone already hit in prod. Odd streaming chunks, retry edge cases, timeouts. Prune it and you rediscover each one at 3am. Who patches this when a provider changes its response format?

1. Not a tangent, it’s related to the very first content visible on that link. 2. Not a dismissal, an advise from an expert 3. Not complaining, as stated, giving an advise about the optics of using clear LLM prose on the first paragraph

Tangential annoyance?