UpTrain is an open-source platform for evaluating and improving generative AI applications. It grades LLM outputs with more than twenty preconfigured checks โ covering response quality, context use, factual grounding, and safety โ and performs root cause analysis on the failures so teams know what to fix. The project has earned more than 2,300 stars on GitHub and was last actively updated in August 2024.
Shipping an LLM feature is easy; knowing whether it is actually good is the hard part. UpTrain gives teams a systematic way to score production and test traffic: checks such as response completeness, factual accuracy, context relevance, and retrieval quality run automatically, and low-scoring cases are grouped with explanations of likely root causes. Evaluations can run through a locally hosted dashboard, keeping data on your own machine, or programmatically through the Python package inside your pipeline. Safety checks cover problems like jailbreak attempts and prompt injection, and custom evaluators let you encode your own quality criteria. The team has also published a prompt drift monitoring benchmark showing how the same checks can track quality over time as models and prompts change in production.
The open-source version of UpTrain is free to self-host under the Apache-2.0 license. UpTrain also offers managed, hosted options for teams through its website, making the overall product freemium.
UpTrain is for teams building chatbots, RAG systems, and other LLM applications who need continuous quality measurement rather than vibes. It suits ML engineers and product teams setting up evaluation gates before releases and monitoring for quality drift in production.