Sublingual: LLM evals for lazy devs

We've all done it: skipping evals, testing on vibes, and ripping it straight to prod. Integrating observability and evals can be a lot of work, which is why Sublingual gives you deep insights into how your LLMs perform without touching your code. It works across a wide range of environments, capturing extensive logs including LLM interactions, inputs, outputs, and server call data. Just pip install subl and you’re live.

https://www.youtube.com/watch?v=yoki32IJXBg

How we got here:

We’ve spent years building and researching LLM applications, and we’ve seen firsthand how developers handle evaluation: sifting through logs, relying on intuition, and struggling with the friction of integrating existing observability tools when they just want to focus on building. Through conversations with numerous founders, we've learned that they're often too busy building to establish robust evaluation systems. So, they end up relying on a couple vibe tests before crossing their fingers and pushing to prod.

That’s why we built Sublingual—effortless LLM observability that works out of the box. No code changes, no distractions, just the insights you need to ship with confidence.

Our approach:

Minimize onboarding overhead: We’ve hacked away at the complexity of automating integration so you can start collecting insights instantly. Our design prioritizes minimal friction, ensuring observability works out of the box without interrupting your workflow.
Minimize intrusiveness: Disentangle logging and LLM serving logic to ensure logging server failures never impact LLM serving reliability. Sublingual is designed to be plugged in or removed without affecting any functionality of your code.
Easy local hosting: Our tool and stored logs are entirely local, so there’s no risk of data leakage.
Code insights: We use a mix of static and dynamic code analysis to deeply understand your program, extracting details that other platforms can’t like automatically finding prompt templates.