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
The 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".
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.
Open 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.
Amazing! 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.
What online signal recalibrates simulated rankings against actual task success? Also do you have a plan to support semantic caching at the router level?
For 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.
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
The 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".
But then it's better to just not have a gateway switch models at all.
Just 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.
and caching is related to performance too ofc
Open 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.
Ans: we rarely switch, often times it's just a "switch to using this model for your agent"
>The gateway adds under 1 ms for BYOK requests
Amazing! 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
What online signal recalibrates simulated rankings against actual task success? Also do you have a plan to support semantic caching at the router level?
For 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.
Very cool. Does your gateway decide effort levels as well? Or just models?
Yep! One interesting example is often Opus 5 on low reasoning ~= Opus 5 on high reasoning.
what's the business model here. How does experiential labs make money
They make money on enterprise plans: https://www.experientiallabs.ai/pricing#enterprise
Look 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
You started it a week ago? I look forward to checking back in 3 weeks when you've exited for $1B
Looks like first PR is June 24th: https://github.com/experientiallabs/experiential/pull/1
So, two months. Still impressive!
See you soon
I have not tried it yet. Is it similar to LiteLLM? If so, what sets it apart?
Router and model optimization from traffic is the main differentiator
Also a hosted marketplace, not just BYOK
Super interesting and congrats on the release. Curious if you initially had this in Python and then rewrote in Rust?
Yep! If you look at the commit history that's exactly what happened.
Finally an open source tool doing this!