This looks absolutely brilliant idea to track and stay informed about the performance of LLM applications. I am in the process of building one and certainly give it a try to measure my LLM app.
Stellar work @max_deichmann , @marc_klingen , @clemo_sf ! If anything, I dare to say a bit too good for a launch ;). Stoked to test it out on some projects - godspeed!
Have been using Langfuse for analytics for our chatbots and have to say its quite well done! Kudos to the team for such an amazing execution! I am definitely going to be a long term user.
We’ve been unsung Langfuse for 2 months now. It’s easy to integrate and makes it simpler for us to monitor & debug LLM requests during development and beyond.
Langfuse has been indispensable for us in leveraging LLMs. Its detailed tracing provides unrivaled clarity, streamlining our debug times and offering insights into both LLM and non-LLM actions. With seamless integrations, from Langchain to their SDKs, and insightful analytics on cost, feedback, and latency, it's the must-have tool for anyone in the LLM space. A bonus: their commitment to open source means flexibility for developers and is in line with the decentralized nature we love in crypto. Kudos to the Langfuse team!
How does Langfuse manage to streamline the process of exploring complex logs and traces, making it efficient and user-friendly? Exciting to see how this tool can elevate app observability and analytics!
