Fine-tune on your LangSmith traces with Baseten Loops

LangChain has introduced LangSmith Fine-Tuning, a workflow that lets users convert successful LangSmith agent traces into supervised fine-tuning datasets for open models using the open-source smithtune CLI. Training is executed through Baseten Loops on dedicated GPUs within a user’s Baseten workspace, using the Loops Python SDK and the user’s own API key and billing account. The CLI checks which models are available for training before jobs are scheduled, saves checkpoints directly on Baseten, and can deploy evaluated checkpoints to Baseten Dedicated Inference without requiring model-weight transfers. The integration is intended to give teams with production agent traces a relatively direct path to custom models optimized for their own agents, including through coding-agent-assisted setup. Smithtune is available on GitHub, while Baseten Loops remains in early access and may require workspace enablement.

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