LangSmith lists Jev as a model option for online trace monitoring
LangSmith names Jev among the model options for online trace monitoring, alongside clustering, turning errors into evals and other tools that help teams read agent run data.
yup, spot on! Traces are the biggest asset for teams to improve their agents over time with Evals, Environments, & human digestible reports explaining what users want with LangSmith we obsess over tooling to help every team understand their agent traces at scale - online monitoring of every trace (custom models, Jev, super cheap small models, built-in rubrics) - clustering traces and errors into human digestible views - turning errors into evals that can be hill-climbed - and open sourcing tooling to build Harbor Environments for Evals or RL from your data we’re very “look at the data” pilled, there’s alpha hiding in there and we want to help you find it more coming soon on helping every team use their valuable Trace data for post-training 👀
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