Could you say more about how caching works? One major advantage of sticking with a single model is saving money on cached input tokens. I'd imagine if you swap between a bunch of models, you may improve performance but cost would would balloon out of control
replyThe trick is to rarely switch, or switch at task boundaries. Often the conclusion of routing is actually "this one model is actually at the pareto front for this task, just use it always".
replyBut then it's better to just not have a gateway switch models at all.
replyJust have the harness able to choose which model its sub-agents use, then tell it how to split up tasks and which models to use when doing so.
That is another way to do. Or we can automatically figure out which models the subagents should be using for you. And update them as new models come out and the work your subagents do changes. More than one way to skin a cat.
replyand caching is related to performance too ofc
replyWhat online signal recalibrates simulated rankings against actual task success? Also do you have a plan to support semantic caching at the router level?
replyFor the online signal, we use a LLM judge with a rubric calibrated offline by the user via TUI. UX of the calibration is a major focus area. Semantic caching is interesting, open to supporting it but not currently planned.
replyOpen source and no markup is the right default for a gateway. The caching question above is the one I would want answered before swapping models though.
replyAns: we rarely switch, often times it's just a "switch to using this model for your agent"
reply>The gateway adds under 1 ms for BYOK requests
replyAmazing! Really brilliant idea, thank you for sharing this project. There is so much ground to cover in the LLM gateway / routing / reporting world, and this is a great start. The Tinker implementation is my favorite part, fine tuning is much better than a sea of context files.
Thanks! We are going to add continual RL via Tinker soon too
replyVery cool. Does your gateway decide effort levels as well? Or just models?
replyYep! One interesting example is often Opus 5 on low reasoning ~= Opus 5 on high reasoning.
replywhat's the business model here. How does experiential labs make money
replyThey make money on enterprise plans: https://www.experientiallabs.ai/pricing#enterprise
replyLook at the Intelligence features in the Enterprise plan:
* Per-prompt model optimization
* Caching
* A model you own, trained on your traffic
yep, it will be through enterprise licenses and our own hosted platform built on the repo
replyYou started it a week ago? I look forward to checking back in 3 weeks when you've exited for $1B
replySee you soon
replyI have not tried it yet. Is it similar to LiteLLM? If so, what sets it apart?
replyRouter and model optimization from traffic is the main differentiator
replyAlso a hosted marketplace, not just BYOK
replySuper interesting and congrats on the release. Curious if you initially had this in Python and then rewrote in Rust?
replyYep! If you look at the commit history that's exactly what happened.
replyFinally an open source tool doing this!
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