Introducing LangSmith Fine-Tuning

LangSmith Fine-Tuning and its smithtune CLI are introduced as a public-beta workflow for converting LangSmith agent trajectories into specialized supervised fine-tuned models, with managed training through Fireworks or Baseten and evaluation in LangSmith. Agent trajectories capture the full sequence of messages, tool calls, results, and changing tool context needed to reproduce successful behavior, allowing teams to curate “golden” examples, create auditable datasets, filter for model compatibility, and split data for training and evaluation. The CLI supports planning training configurations, LoRA-based managed training without GPU administration, validation-based checkpoint selection, replay evaluations comparing tuned and base models, and deployment of successful models. Internal experiments reported that fine-tuning Kimi K3 improved an issue-detection benchmark score from 90.0 to 96.0, while fine-tuned Qwen-3.8-27B improved code-review F1 from 48.9% to 53.7% while reducing model calls and tool requests by about 30%. The announcement emphasizes that careful data selection and review are crucial, notes that an earlier less-selective dataset reduced performance, and recommends fine-tuning primarily for recurring tasks where teams have clear examples of desired agent behavior after harness-engineering improvements have been explored.

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