UpTrain – Open-Source LLM Evaluation Platform

TL;DR

TL;DR: UpTrain is an open-source LLM evaluation platform that runs 20+ pre-configured checks (factual accuracy, response completeness, context conciseness, and more) with root cause analysis on failures and a local-first dashboard. Apache-2.0 licensed.

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 LICENSE file on main branch)
• HN launch thread: news.ycombinator.com/item?id=35069839
• Demo: demo.uptrain.ai/evals_demo

What Is UpTrain?

UpTrain is an open-source unified platform to evaluate and improve Generative AI applications. According to the official README, it provides grades for 20+ pre-configured evaluations covering language, code, and embedding use cases, performs root cause analysis on failure cases, and gives actionable insights on how to resolve them.

Key capabilities:

• 20+ pre-configured evaluation checks — Response Completeness, Factual Accuracy, Context Conciseness, tonality, code syntax, and more
• Root cause analysis — drill into specific failure modes in your LLM pipeline
• Local-first dashboard — runs on your machine; data never leaves your environment
• Flexible evaluator LLMs — use OpenAI, Anthropic, Mistral, Azure OpenAI, or self-hosted open-source models via Anyscale
• Experiment tracking — compare evaluation scores across embedding models and prompt configurations

Setup

UpTrain is available on PyPI. The dashboard runs locally.

pip install uptrain

Running the Dashboard

After installation, launch the local evaluation dashboard:

uptrain dashboard

This starts the web interface on your local machine where you can configure evaluations, run checks against your LLM outputs, and view results.

Python Integration

UpTrain also integrates directly in Python scripts:

from uptrain import EvalLLM, Evals

eval_llm = EvalLLM(openai_api_key="sk-...")

results = eval_llm.evaluate(
    data=[{"question": "...", "response": "..."}],
    checks=[Evals.RESPONSE_COMPLETENESS, Evals.FACTUAL_ACCURACY],
)

Practical Evaluation Checklist

When evaluating an LLM application with UpTrain:

• Define your primary metrics (completeness, factual accuracy, conciseness)
• Run baseline evaluation before prompt changes
• Use root cause analysis on the lowest-scoring checks
• Compare evaluator model choices (OpenAI vs. Anthropic vs. open-source)
• Track experiment runs to observe score drift over time

Security Notes

• All evaluation runs are local — data does not leave your environment
• LLM API calls (when using model grading) go directly to your configured provider
• No telemetry or external data sharing beyond your own API calls to the evaluator LLM

FAQ

Q: Does UpTrain require an API key? A: Only if you use a hosted evaluator LLM (OpenAI, Anthropic, etc.). Local evaluator models can be used without API keys.

Q: What embedding models does UpTrain support? A: HuggingFace models, Replicate endpoints, or custom endpoints. The README specifically mentions text-embedding-3-large/small, text-embedding-3-ada, and baai/bge-large.

Q: Can I run this offline? A: Yes. The dashboard runs locally and evaluator checks run on your machine. Only the LLM API calls (if using hosted models) require internet access.

Conclusion

UpTrain fills a practical gap for teams shipping LLM-powered products — routine evaluation without spinning up a full ML platform. Its 20+ built-in checks, root cause analysis, and local-first design make it a solid addition to any AI development workflow. Apache-2.0 licensed, with a live demo available at demo.uptrain.ai.