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.
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khalic
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.
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clickety_clack
One of the 2 dependencies, httpx, isn't really maintained anymore. Pydantic picked it up as httpx2: https://pydantic.dev/docs/httpx2
9dev
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
dlojudice
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
nperez
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
freshtake
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.
hopfenspergerj
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.
Also Strongly recommend renaming to avoid confusion
tway235
token counting is useful though. There's also other ways to reduce size, ie a plugin API.
arjie
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.
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asveikau
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.
DrStartup
most software like this will be dematerialized, democratized, and demonetized - companies building in the infra band being increasingly disintermediated
LeBit
How does it compare to Bifrost?
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TZubiri
Drop the Lite, it's cleaner, just use the godamn LLM directly.
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.
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.
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
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
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
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.
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
Also Strongly recommend renaming to avoid confusion
token counting is useful though. There's also other ways to reduce size, ie a plugin API.
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.
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.
most software like this will be dematerialized, democratized, and demonetized - companies building in the infra band being increasingly disintermediated
How does it compare to Bifrost?
Drop the Lite, it's cleaner, just use the godamn LLM directly.