OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Evaluation & Observability

Uptrain

UpTrain is an open-source Python tool for evaluating and improving generative AI applications. It grades results from more than 20 preconfigured checks across language, code and embedding use cases, and analyzes failure cases to help identify causes and possible resolutions. It offers a locally hosted dashboard and a Python package for programmatic evaluation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Uptrain — OpenViST project profile in AI Evaluation & Observability.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use UpTrain when you need repeatable checks for generative AI outputs or a way to inspect evaluation failures, whether through its local dashboard or Python package.

Who it is for

  • Developers evaluating generative AI application outputs
  • Teams seeking a dashboard for evaluation results and failure analysis
  • Practitioners building checks for language, code or embedding workflows

Documented uses

  • Grade generative AI results using preconfigured checks
  • Review evaluation outcomes through a locally hosted dashboard
  • Investigate failure cases and identify possible resolutions
  • Integrate evaluations programmatically through the Python package
Before you choose

Requirements & limits

The project is open source under the Apache-2.0 license. The dashboard setup requires Docker. The README’s Python evaluation example uses an OpenAI API key.

  • Choose the dashboard route for a web interface that the README says does not require writing code; Docker is required for setup.
  • Choose the package route for programmatic use; the documented evaluation example uses an OpenAI API key.
  • The repository describes more than 20 preconfigured checks and marks the dashboard beta.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Start the locally hosted UpTrain dashboard

Set up the documented self-hosted dashboard on a local machine.

Before you start

  • Docker installed
  • Git and a local command-line environment

Steps

  1. Clone the UpTrain repository.

  2. Change into the uptrain directory.

  3. Run bash run_uptrain.sh to start the dashboard locally.

Expected outcome

A locally hosted web dashboard for evaluating LLM applications and viewing results and root-cause analysis.

Guide limitations

  • The README labels the dashboard beta.
  • These setup steps do not specify an evaluation procedure or application-specific configuration.
Documentation 1 ↗
Source-based practical guide

Install the UpTrain Python package

Install the package for programmatic evaluation workflows.

Before you start

  • Python environment with pip

Steps

  1. Run pip install uptrain.

  2. For the README’s open-source evaluation example, provide an OpenAI API key.

Expected outcome

The UpTrain package is installed; the documented example identifies the credential needed to run its evaluation workflow.

Guide limitations

  • The OpenAI API key requirement is stated for the example, not all possible workflows.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
uptrain-ai/uptrain · Official homepage title · retained in its source language
How we review sourcesSuggest a correction