The use case: how LangChain deploys Baseten
LangChain uses Baseten Loops to fine-tune custom open-weight models for LangSmith Engine, its in-platform agent that helps users debug and improve their agents. Models are trained on agent traces for tasks like diagnosing issues from a GitHub repository and categorizing traces, then deployed through Baseten's inference platform.
In the customer’s words
Baseten is a key partner as we develop the models behind LangSmith Engine. Loops gives us the control and iteration speed we need while training, backed by a team that works closely with us. Having training and inference on the same platform gives us a clear path from model development to production.Other AI tools used by LangChain
Other public relationships recorded on usedby.
Explore LangChain’s AI stack
Baseten users in DevTools & Infrastructure
Companies in the same sector, with public evidence.
See all Baseten customers
Go straight to the source
Based on Baseten’s published case study. We check the public evidence; the results are reported by the vendor.
baseten.co- Last checked
- Nov 7, 2026
Open source capture What the story claims, and what we checked
What we compared with the page.
- The passage quoted aboveChecked
Copied word for word from the page, near the name of LangChain.
- LangChain uses BasetenChecked
Confirmed line.
- The result itselfNot checked
We quote it; we did not measure it.
Fine-tuning a large open-weight model on agent traces specializes it for difficult tasks in LangSmith Engine, such as reading a connected GitHub repository to diagnose why an issue is happening, then drafting the prompt or code change that goes into the pull request.