We are starting to role out our Trace Judge model to early partners today
Designed to detect errors in agent trajectories at 1/100th of the cost compared to closed models
If interested in early access, sign up on the form below: https://airtable.com/appWdRBlSecNOgErA/pagAEfUlHu4F35opm/form
Users praised LangChain's Trace Judge Model for its 1/100th cost with comparable accuracy to closed alternatives, seeing it as the right approach to scale agent reliability via cheap validation.
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The product of some of the work the LangChain Labs team has been up to recently 📌
You can read more about it here - https://www.langchain.com/blog/building-a-100x-cheaper-trace-judge-with-fireworks - and try it out for yourself by signing up below
We are starting to role out our Trace Judge model to early partners today
Designed to detect errors in agent trajectories at 1/100th of the cost compared to closed models
If interested in early access, sign up on the form below: https://airtable.com/appWdRBlSecNOgErA/pagAEfUlHu4F35opm/form
@VagKos @hwchase17 When there is a 'perceived error' in the conversation trajectory between a user and the agent - ie user frustration, the conversation ends abruptly, the agent needs to correct itself, the user has to repeat themselves etc.
@hwchase17 What do you mean by errors in agent trajectories? A simple example would probably help :). I run a lot of agents so I am quite interested in this!
@hwchase17 1/100th cost on trajectory eval is the unlock
@hwchase17 that cost efficiency is seriously impressive, how did you manage that?
@jakebroekhuizen @hwchase17 Thanks Jake! that makes sense
@hwchase17 1/100th cost with comparable accuracy to closed models is the right vector — agent reliability scales with cheap validation, not expensive inference.
