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

• 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
```bash

### Running the Dashboard

After installation, launch the local evaluation dashboard:

```bash
uptrain dashboard
```bash

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:

```python
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.