UpTrain – Open-Source LLM Evaluation Platform

TL;DR

TL;DR: UpTrain is an open-source platform that evaluates LLM applications using 20+ pre-built checks (response quality, factual accuracy, jailbreak detection) and a self-hosted dashboard for root cause analysis.

Source and Accuracy Notes

⚠️ This section is MANDATORY. All links must be verified from actual source, not guessed.

• Project page: uptrain.ai
• Source repository: github.com/uptrain-ai/uptrain
• License: Apache-2.0 (verified via GitHub API)
• Latest release: v0.7.1 (verified via GitHub Releases API)
• Stars: 2,352 (verified via GitHub API)
• HN launch thread: news.ycombinator.com/item?id=41505923

What Is UpTrain?

UpTrain is an open-source unified platform for evaluating and improving Generative AI applications. It provides grades for more than 20 preconfigured evaluation checks covering language, code, and embedding use cases. The platform performs root cause analysis on failure cases and surfaces insights on how to resolve them.

The project description reads:

“UpTrain is an open-source unified platform to evaluate and improve Generative AI applications. We provide grades for 20+ preconfigured evaluations (covering language, code, embedding use-cases), perform root cause analysis on failure cases and give insights on how to resolve them.”

UpTrain runs entirely on your local machine via a self-hosted dashboard. Data never leaves your environment except when LLM calls are made using model grading checks (e.g., calling OpenAI or Anthropic APIs for evaluation).

Setup Workflow

Option A: Self-Hosted Dashboard (No Code Required)

The dashboard runs locally via Docker. No coding needed.

1. Install Docker on your machine if you do not have it already. Install from docker.com/get-started.
2. Pull and run the UpTrain dashboard:

docker run -p 3000:3000 uptrain/uptrain-dashboard

3. Open http://localhost:3000 in your browser.
4. Start evaluating your LLM applications through the web interface.

Option B: Python Package (Programmatic)

For developers integrating UpTrain evaluations into an application:

pip install uptrain

Then evaluate responses programmatically:

import uptrain

data = [
    {"question": "...", "response": "...", "context": "..."}
]

results = uptrain.evaluate(
    data=data,
    checks=[
        uptrain.Evals.CONTEXT_RELEVANCE,
        uptrain.Evals.FACTUAL_ACCURACY,
        uptrain.Evals.RESPONSE_COMPLETENESS
    ]
)

Pre-Built Evaluations

UpTrain ships with 20+ pre-configured evaluation checks:

| Evaluation | What It Checks | |---|---| | Response Completeness | Does the response answer all aspects of the question? | | Response Conciseness | Is the response free of irrelevant information? | | Response Relevance | How relevant was the generated context to the question? | | Factual Accuracy | Is the response factually correct and grounded by context? | | Context Relevance | How relevant was the retrieved context to the query? | | Context Utilization | Was the provided context used completely in the response? | | Code Hallucination | Is the code in the response grounded by the provided context? | | Jailbreak Detection | Is the prompt attempting to jailbreak the LLM? | | Prompt Injection | Is the user trying to reveal system prompts? | | User Satisfaction | How well are user concerns addressed? |

Evaluator LLM Options

UpTrain lets you use any of these as the judging LLM:

• OpenAI (GPT-4, GPT-3.5-turbo)
• Anthropic (Claude)
• Mistral
• Azure OpenAI endpoints
• Open-source LLMs hosted on Anyscale

Root Cause Analysis

When a response fails evaluation checks or receives negative user feedback, UpTrain performs root cause analysis to identify which part of your LLM pipeline is underperforming. The dashboard visualizes failure patterns and suggests concrete fixes.

Security Notes

• The dashboard runs locally on your machine — evaluation data does not leave your environment
• LLM calls for grading checks go to your configured API provider (OpenAI, Anthropic, etc.)
• No data is stored on UpTrain’s servers when using the self-hosted dashboard
• The code is open-source (Apache-2.0), so you can audit the evaluation logic yourself

FAQ

Q: Do I need an API key to use UpTrain? A: Yes, if you use model-based grading checks (LLM-as-a-judge). You can use OpenAI, Anthropic, Mistral, or Azure OpenAI API keys. The local evaluation logic runs without any API calls.

Q: Can I create custom evaluations? A: Yes. UpTrain supports custom guidelines, custom prompts, and entirely custom evaluators beyond the pre-built checks.

Q: How is UpTrain different from OpenAI Evals? A: UpTrain offers a self-hosted dashboard (no code required), 20+ pre-built checks, root cause analysis, and support for multiple LLM providers beyond OpenAI.

Q: Does UpTrain work with non-OpenAI LLMs? A: Yes. You can evaluate responses from any LLM — UpTrain acts as the judge using your choice of evaluator model.

Conclusion

UpTrain fills a real gap in the LLM development workflow: the absence of systematic, automated evaluation. With 20+ pre-built checks, a no-code dashboard, and root cause analysis, it brings rigor to iterative LLM development without locking you into a single provider. Install it via Docker for a zero-code setup, or pip for programmatic use.

• GitHub: github.com/uptrain-ai/uptrain
• Docs: docs.uptrain.ai
• Demo: demo.uptrain.ai