Litelm: LiteLLM Without the Bloat

Posted by kennethwolters 20 hours ago

15954Original

Comments

Comment by skrellm 4 minutes ago

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".

Comment by Centigonal 19 hours ago

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.

Comment by nylonstrung 7 hours ago

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

Comment by geek_at 21 minutes ago

And let's not forget that they regularly break things. For example they had merged a pull request that was supposed to fix issues related to the openrouter/free models but in the process broke openrouter for all models except the free ones.

The fix was deployed 2 weeks later (!!) to main (but you could downgrade of course).

Or that other time they broke model selection if you had selected "this key can used all models of their team" than the only model in the auto-selection for harnesses was an invalid "all-team-models" entry. Fixes this one in 1 week though.

all of them on the :latest docker tag btw

Comment by raybb 6 hours ago

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

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

Comment by more-coffee 4 hours ago

That blog post smells like Opus.

Comment by znpy 6 hours ago

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

Comment by OutOfHere 19 hours ago

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.

Comment by sv123 18 hours ago

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?

Comment by datadrivenangel 18 hours ago

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.

Comment by yujonglee 15 hours ago

thanks for the feedback. genuinely curious what you think we could be doing better, especially around our dev practices. Would love to hear specifics.

Comment by miki123211 2 hours ago

Maybe this has improved since I last used it, but I was dismayed at not being able to do something like:

    litellm.register("foo", CustomAIProvider)
    litellm.do_whatever("foo/my-cool-model", "what is 2+2")

Comment by OutOfHere 14 hours ago

Didn't yall have a release of a package that stole user keys or such?

Comment by otabdeveloper4 4 hours ago

Don't worry about it. Claude did a thorough security review of their code.

Comment by 10 hours ago

Comment by mpyne 16 hours ago

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.

Comment by blazarquasar 5 hours ago

It may not be a huge resource hog, but it adds a ton of latency.

https://www.getmaxim.ai/bifrost/resources/benchmarks

Having ran both LiteLLM and Bifrost for months, I can largely confirm the numbers from those benchmarks for myself.

Comment by otabdeveloper4 4 hours ago

LiteLLM is vibecoded trash.

Comment by khalic 19 hours ago

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.

Comment by bityard 15 hours ago

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.

Comment by jatins 9 minutes ago

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

Comment by xg15 4 hours ago

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.

Comment by moron4hire 13 hours ago

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.

Comment by devinpadron 19 hours ago

Agree. The LLM'isms are offputting.

Comment by VCFundedGenYer 18 hours ago

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

Comment by 0xbadcafebee 18 hours ago

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

Comment by rexpop 18 hours ago

> Avoid generic tangents.

> Please don't post shallow dismissals

> Please don't complain about tangential annoyances—e.g. article or website formats, name collisions, or back-button breakage.

See: Hacker News Guidelines

Comment by khalic 16 hours ago

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

Comment by Barbing 16 hours ago

Tangential annoyance?

Comment by conorcleary 5 hours ago

Radial annoyance declarations only!

Comment by ravenstine 18 hours ago

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.

Comment by 17 hours ago

Comment by mark_l_watson 2 hours ago

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.

Comment by clickety_clack 18 hours ago

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

Comment by 9dev 16 hours ago

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

Comment by dlojudice 16 hours ago

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

Comment by freshtake 19 hours ago

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.

Comment by 19 hours ago

Comment by hopfenspergerj 17 hours ago

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

Comment by isuckatcoding 12 hours ago

Also Strongly recommend renaming to avoid confusion

Comment by BOOSTERHIDROGEN 5 hours ago

Make a docker image rootless please, thanks

Comment by nperez 11 hours ago

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

Comment by DrStartup 16 hours ago

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

Comment by tway235 12 hours ago

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

Comment by asveikau 13 hours ago

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.

Comment by arjie 18 hours ago

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.

Comment by gcgbarbosa 18 hours ago

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.

Comment by arjie 17 hours ago

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

Comment by LeBit 19 hours ago

How does it compare to Bifrost?

Comment by josephh 18 hours ago

I'm always confused by LLM proxies that claim to support tool calling. Even for Bifrost that claims to be doing it, at least when I was checking it out, I found out that while it injects the list of MCP tools that's available on the proxy-side, it doesn't actually make the call on client's behalf, and clients get confused by it (response returns MCP call request whose tool doesn't exist on the client-side).

Comment by robertclaus 18 hours ago

LiteLLM is basically Bifrost in the Python ecosystem.

Comment by gcgbarbosa 17 hours ago

Yeah, but bifrost seems tighter and claims to use way fewer resources

Comment by yujonglee 15 hours ago

hey - litellm will migrate its core to Rust soon. please follow this issue if you're interested! https://github.com/BerriAI/litellm/issues/31263

Comment by yujonglee 15 hours ago

also if you had resource issues with your production deployment, keen to hear more details

Comment by testycool 14 hours ago

I don't have specifics since it's been months, but LiteLLM on a hetzner server, deployed through Coolify was using 1-4 GB (cannot exactly, and dont know why either).

Bifrost feels very light, though.

Will try LiteLLM if it's in Rust.

Would also appreciate a slicker aesthetic. I got used to Bifrost shadcn with 0 border radius.

Comment by yujonglee 14 hours ago

- We recently migrated to shadcn, which also comes with dark mode support. - You can subscribe to the issue or email me at yujong at berri.ai

I’m happy to provide any support if you’re willing to try out the initial version.