All that agent trace data sitting in your production logs — what if it could train its own specialized model? LangSmith just made that a built-in workflow. Title: Introducing LangSmith Fine-Tuning URL: ❓ What is LangSmith Fine-Tuning? It's the `smithtune` CLI, which turns production agent traces into a fine-tuned model without building custom infrastructure. It covers dataset creation, training, evaluation, and deployment end to end. ❓ How does it build the training data? It pulls trajectories — ordered sequences of messages and tool calls — from LangSmith projects, then uses a multi-agent review process with custom rubrics to keep only high-quality examples. Crucially, it preserves the exact context at each turn, including which tools were available. ❓ How do training and deployment work? Supervised fine-tuning via LoRA runs through managed platforms like Fireworks and Baseten, so there's no GPU provisioning to manage. After a replay evaluation against the base model, `smithtune deploy` ships the fine-tuned model straight to production. ❓ What results did they see? On an issue-detection task, fine-tuning lifted Kimi K3's score from 90.0 to 96.0. On code review, it matched or beat base-model quality while cutting model calls by 29.8% and tool requests by 29.4%. #LangSmith# #FineTuning#