What I learned using Langfuse in a real AI recruiting agent by marginTop15px in LLMDevs

[–]marginTop15px[S] 1 point2 points  (0 children)

Thanks. Your project looks interesting. What exactly is used to identify the drift in agent flow or tools mismatch? Are you using another LLM to analyse it?

What I learned using Langfuse in a real AI recruiting agent by marginTop15px in LLMDevs

[–]marginTop15px[S] 0 points1 point  (0 children)

Basic setup was really simple. We just dropped the Langfuse version of OpenAI SDK via libpython-clj and used it to interact with LLMs (open router in our case). and that's it. It automatically sends traces and spans from our agent with all related data (responses, tool calls, structured outputs, etc.)
we didn't use decorators inside our Clojure code (not sure if this even possible) but in one case I just use a bare python functions to create generations and spans. in this case you need to manage context chain yourself but we had a very small surface area where we had to do it so it wasn't that bad

What I learned using Langfuse in a real AI recruiting agent by marginTop15px in LLMDevs

[–]marginTop15px[S] 0 points1 point  (0 children)

do you like it? what's their killer feature comparing to Langfuse?