Sublingual is an open-source LLM observability and evals platform that requires zero code changes to integrate. Instantly plug into a full suite of analysis tools with just one pip install—nothing else.
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
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.
Try it out right now, it will take less than a minute! If you don’t like it, just uninstall and your code will be exactly the same as before.
⭐ Give our project a star 😊
🧑💻 GitHub: https://github.com/sublingual-ai/sublingual
🔗 Website: https://sublingual.ai
Dylan (CTO): previously LLM research at UIUC Kang Lab and Dept. of Defense
Matthew (CEO): previously TikTok ML on the recommendation algorithm and ads engine, LLM research for rec-sys at Nextdoor
Contact us: